Multifactorial Analysis in Corpus Linguistics (Open Linguistics Series)

Multifactorial Analysis in Corpus Linguistics

Open Linguistics Series Series Editor Robin Fawcett, University of Cardiff This series is 'open' in two related ways. First, it is not confined to works associated with any one school of linguistics. For almost two decades the scries has played a significant role in establishing and maintaining the present climate of'openness' in linguistics, and we intend to maintain this tradition. However, we particularly welcome works which explore the nature and use of language through modelling its potential for use in social contexts, or through a cognitive model of language - or indeed a combination of the two. The series is also 'open' in the sense that it welcomes works that open out 'core' linguistics in various ways: to give a central place to the description of natural texts and the use of corpora; to encompass discourse 'above the sentence'; to relate language to other scmiotic systems; to apply linguistics in fields such as education, language pathology and law; and to explore the areas that lie between linguistics and its neighbouring disciplines such as semiotics, psychology; sociology', philosophy, and cultural and literary studies. Continuum also also publishes a series that offers a forum for primarily functional descriptions of languages or parts of languages - Functional Descriptions of Language. Relations between linguistics and computing are covered in the Communication in Artificial Intelligence series. Two series, Advances in Applied Linguistics and Communication in Public Life, publish books in applied linguistics and the series Modern Pragmatics in Theory and Practice publishes both social and cognitive perspectives on the making of meaning in language use. We also publish a range of introductory textbooks on topics in linguistics, semiotics and deaf studies. Recent titles in the series Culturally Speaking. Managing Rapport through Talk across Cultures, Helen Spcnccr-Oatey (ed.) Educating Eve: The 'Language Instinct'Debate, Geoffrey Sampson Empirical Linguistics, Geoffrey Sampson Genre and Institutions: Social Processes in the Workplace, and School, Frances Christie and J. R. Martin (eds.) Pedagogy and the Shaping of Consciousness: Linguistic and Social Processes, Frances Christie (cd.) Words, Meaning and Vocabulary: An Introduction to Modern. English Lexicology, Howard Jackson and Etienne Ze Amvcla Syntactic Analysis and Description: A Constructional Approach, David G. Lockwood Relations and Functions within and around Language, Peter H. Fries, Michael Cummings, David G. Lockwood and William Spruiell (eds) Classroom Discourse Analysis: A Functional Perspective, Frances Christie Working with Discourse: Meaning Beyond the Clause,]. R. Martin and David Rose

Multifactorial Analysis in Corpus Linguistics A Study of Particle Placement

Stefan Thomas Gries

Continuum The Tower Building, 11 York Road, London, SE1 7NX 370 Lexington Avenue, New York, NY 10017-6503 This edition published by Continuum 2003 © Stefan Thomas Cries 2003 This work has been accepted as the author's PhD dissertation by the Faculty of Language Sciences, University of Hamburg, on 27 September 2000; supervisors: Klaus-Uwe Panther and Giinter Radden. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage or retrieval system, without prior permission in writing from the publisher. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. ISBN 0-8264-6126-3 (hardback) Library of Congress Cataloging-in-Publication Data Grics, Stefan Thomas. 1970 Multifactorial analysis in corpus linguistics : a study of particle placement / Stefan Thomas Gries. p. cm. (Open linguistics series) Originally presented as the author's PhD thesis - University of Hamburg, 2000. Includes bibliographical references and index. ISBN 0-8264-6126-3 1. English language - Particles. 2. English language - Word order. 3. Corpus linguistics. I. Title. II. Series. PE1321 .G742002 425 - dc21

2002071250 Typeset by RetineCatch Limited. Bungay, Suffolk Printed and bound in Great Britain by MPG Books Ltd, Bodmin. Cornwall

Contents

List of figures figures

vii

List of tables

ix

Preface

xi

List of abbreviations

xii

1 Introduction 1.1 The scope of the study 1.2 The diachronic development of phrasal verbs 1.3 Theoretical assumptions 1.4 Outline of the study

1 1 3 5 1

2 Review of literature 2.1 Phonological variables 2.2 Morphosyntactic variables 2.3 Semantic variables 2.4 Discourse-functional variables 2.5 Other variables 2.6 Interim summary and critical evaluation

12 12 13 15 18 21 22

3 Objectives of this study

44

4 Key notions and hypotheses 4.1 Discourse-functional variables 4.2 Semantic variables 4.3 Morphosynlactic variables 4.4 Phonological variables 4.5 Remaining variables 4.6 interim summary

48 49 52 56 58 58 61

vi

CONTENTS

5 The data 5.1 Origin of the corpus data 5.2 Treatment of the. corpus data

67 67 69

6 Results and discussion 6.1 Monofactorial results 6.2 Pair-wise comparisons: the relative strengths of variables 6.3 Multifactorial results 6.4 Further evaluation

79 79 101 107 118

7 General discussion 7.1 Prototypes 7.2 Variability and grammar

132 132 143 146

7.3 Competing syntactic variation variation 7.3 Competing approaches approaches to to syntactic

8 The activation of constructions 8.1 Theoretical introduction 8.2 The, relation of variables to activation 8.3 A network of variables and weighted (causal) relations 8.4 Interim summary

157 157 166 174 180

9 Conclusion and outlook 9.1 Summary 9.2 Outlook: implications and extensions

185 185 187

10 Appendices 10.1 List of variables 10.2 Register-dependent interaction plots 10.3 List of TPVs

192 192 194 203

11 References

211

Subject index Author index

223 225

Figures

3:1 4:1 4:2 6:1 6:2 6:3 7:1 7:2 7:3 7:4 7:5 8:1 8:2 8:3 8:4 8:5 8:6 8:7 8:8 10:1 10:2 10:3 10:4 10:5 10:6

A piece of discourse up to the point of decision for a construction Concepts and their activation cost for the hearer in a communication situation Determinants of processing effort and particle placement Interaction plot: construction X REGISTER X COMPLEX Importance of predictor variables for CART Distribution of construction predictions relative to kinds of direct objects Discriminant scores of sentences in relation to the prediction accuracy Determinants of processing effort and particle placement Possible explanation of Hawkins's findings 1 Possible explanation of Hawkins's findings 2 Possible explanation of Hawkins's findings 3 Step 1 of the generation of the utterance John picked up books/books up Step 2 of the generation of John picked up books/books up/ John lifted books ' Step 3 of the generation of the utterance John picked up books/books up Step 4 of the generation of the utterance John picked up books / books up Step 5 of the generation of the utterance John picked books up Step 5 of the generation of the utterance John picked books up A network of variables with intercorrelations/association strengths A subpart of the proposed causal activation network Interaction plot: construction X REGISTER X COMPLEX Interaction plot: construction X REGISTER X LENGTIIW Interaction plot: construction x REGISTER x LENGTHS Interaction plot: construction X REGISTER X TYPE Interaction plot: construction X REGISTER X DET Interaction plot: construction X REGISTER X IDIOMATICITY

45 50 61 83 117 123 135 148 149 150 150 162 162 163 164 165 166 177 179 194 194 195 195 196 196

viii

10:7 10:8 10:9 10:10 10:11 10:12 10:13 10:14 10:15 10:16 10:17 10:18

FIGURES

Interaction plot: construction x REGISTER X CONCRETE Interaction plot: construction x REGISTER X ANIMAOY Interaction plot: construction X REGISTER X LM Interaction plot: construction X REGISTER x AcrrPC Interaction plot: construction x REGISTER X TOPM Interaction plot: construction X REGISTER X ConPC Interaction plot: construction X REGISTER X NM Interaction plot: construction X REGISTER X Ci.usSC Interaction plot: construction X REGISTER x TOSM Interaction plot: construction X REGISTER X ConSG Interaction plot: construction X REGISTER X OM Interaction plot: construction X REGISTER X PP

197 197 198 198 199 199 200 200 201 201 202 202

Tables

1:1 2:1 2:2 2:3 2:4 2:5 2:6 2:7 2:8 5:1 5:2 5:3 5:4 5:5 6:1 6:2 6:3 6:4 6:5 6:6 6:7 6:8 6:9 6:10 6:11 6:12 6:13 6:14 6:15 6:16 6:17

Comparison of particle positions in different language stages Entrenchment hierarchy of Grics (1999: 122-6), based on Deane (1987, 1992) ' Variables that are argued to contribute to particle placement Partial results of Peters (1999): absolute frequencies and column percentages The entrenchment hierarchy and its constitutive subparls Fictitious analysis of the complexity of the direct object 1 Fictitious analysis of the complexity of the direct object 2 Fictitious analysis of the complexity of the direct object 3 Fictitious analysis of the complexity of (he direct object 4 Distribution of the 403 sample sentences Encoding of JVP Jype of the Direct Object (TYPE) Encoding of Determiner of the Direct Object (Dm} Encoding of Complexity of the Direct Object (COMPLEX) Example sentence with context from the British National Corpus (File: A91) Observed distribution of constructions relative to COMPLEX Expected distribution of constructions relative to COMPLEX Contributions to Chi-squarc for the distribution in Table 6:1 Distribution of constructions relative to EENGTH\\' Distribution of constructions relative to LENGTHS Distribution of constructions relative to TYPE Distribution of constructions relative to DET Distribution of constructions relative to IUIOMATTCITY Distribution of constructions relative to CONCRETE Distribution of constructions relative to ANIMACY Distribution of constructions relative to LM Distribution of constructions relative to AcxPC (DTLM) Distribution of constructions relative to TOPM Distribution of constructions relative to ConPC Distribution of constructions relative to NM Distribution of constructions relative to CLusSC (DTNM) Distribution of constructions relative to TOSM

4 17 23 28 30 32 32 33 33 68 70 70 71 73 80 81 81 84 84 85 86 87 88 88 89 90 91 92 92 93 94

x

6:18 6:19 6:20 6:21 6:22 6:23 6:24 6:25 6:26 6:27 6:28 6:29 6:30 6:31 6:32 6:33 6:34 6:35 6:36 6:37 6:38 6:39 6:40 7:1 8:1

TABLES

Distribution of constructions relative to ConSC Distribution of constructions relative to OM Distribution of constructions relative to PP Distribution of constructions relative to PART = PREP Observed distribution of constructions relative to disfluency (DISFLUENCY) Distribution of constructions relative to the register (REGISTER) Correlational strength of each variable Variables and values/levels to be contrasted Division of variables into two classes (dichotomization) Distribution of constructions with COMPLEX: simple and DET: indefinite Distribution of constructions relative to COMPLEX and LM Strength of levels of variables in terms of construction distributions Overall results of the first discriminant analysis Factor loadings of the discriminant analysis for all variables Prediction accuracies of three analyses Overall results of the discriminant analysis for the Processing Hypothesis Factor loadings of the discriminant analysis for the Processing Hypothesis Classification/prediction accuracies of three analyses Parameters and settings of the CART analysis Cross-validated prediction accuracies of CART for split samples Differences of average values for correct and false predictions The effect of structural priming on particle placement Comparison of chosen vs. non-chosen constructions (segment transitions) Hawkins's results (1994: 181) concerning particle placement in English Structural equation modelling results

94 95 95 96 97 97 98 103 103 104 105 106 109 110 112 113 114 115 116 117 1 19 120 121 148 178

Preface

This book grew out of my doctoral dissertation submitted to the Faculty of Language Sciences of the University of Hamburg, Germany, in April 2000. While I would like to express my gratitude to all members of the committee, some of them definitely deserve to be singled out. I thank Klaus-Uwe Panther, my main advisor, and Giinter Radden for granting me the enormous intellectual freedom to approach my subject of interest from the quantitative perspective I have gradually been adopting in my research. I am also grateful to Matthias Burisch for spending so much time discussing statistical issues with me. A special word of thanks is due to Thomas Berg, whose influence, especially on the revision of the work for publication, has been tremendous. Last but not least I am grateful to Robin Fawcett, the editor of this series, for his feedback and advice. From many other people whose feedback has contributed to this work, I would like to single out two and thank Anatol Stefanowitsch and Viola L'Hommedieu for many discussions and support during the completion of this project. I would also like to express my heartfelt appreciation to my colleagues in the IFKI at the University of Southern Denmark at Sonderborg. They created a working atmosphere that made it easy for me to devote so much effort to this work. The financial support of the department enabled me to travel a lot to broaden my horizons and get valuable feedback on various parts of this study. Finally, I thank Stefanie WulfF for her patience, her loyalty and her support during all of the stages of my work.

List of abbreviations

TPV VPC

transitive phrasal verb verb-particle construction COMPLEX Complexity of the direct object of the VPC LENGTHS / LENGTHW Length of the direct object in syllables/words NP type of the direct object of the VPC TYPE DET Determiner of the direct object of the VPC CONCRETE Concreteness of the referent of the direct object of the

VPC LM

Last mention of the referent of the direct object of the

VPC AcTPC (DTLM) TOPM ConPC

NM CLusSC (DTNM) TOSM CoHSG

OM PP PART = PREP CART IAM EIC ratio

Activation of the referent of the direct object of the VPC due to the preceding context (distance to last mention) Times of mention of the referent of the direct object of the VPC in the preceding context Coherence of the referent of the direct object of the VPC to the preceding context Next mention of the referent of the direct object of the VPC Clustering of the referent of the direct object of the VPC to the following context (distance to next mention) Times of mention of the referent of the direct object of the VPC in the subsequent context Coherence of the referent of the direct object of the VPC to the subsequent context Times of mention of the referent of the direct object of the VPC in a window of 10 clauses before and after the VPC Presence of a directional adverbial/PP after the VPC The particle of the VPC is identical to the preposition of a following PP Classification and Regression Trees Interactive Activation Model EarlvTmmcdiate-Constitucnt Ratio

1 Introduction

1.1 The scope of the study A phenomenon that has attracted considerable interest in linguistics over the last decades is the existence of what Lambrecht (1994: 6) has, following Danes (1966), referred to as allo-scntences, that is 'semantically equivalent but formally and pragmatically divergent sentence pairs'. Frequently, the phenomenon in question has also been referred to as grammatical variation (cf. Rohdenburg and Mondorf, 2003), syntactic variation (Altenberg 1982: 11-12; Bolkcstcin and Risselada 1987:'497-8) and corifigurational or permutational variation (Stucky 1987: 377-8). Depending on the nature of the formal divergence between the sentence pairs, one can distinguish instances of syntactic variation where the formal divergence results from a different linear arrangement of the constituents only such as Topicalization or Left-Dislocation (e.g. / don't like your elder sister vs. Tour elder sister, I don't like] from those instances where the divergence results from a change of both specific constituents in the sentence pairs and their linear arrangement such as the Dative Alternation (e.g. He gave the book to the man vs. He gave the man the book) or the English genitive (e.g. the book of my father vs. my father's book). In this study, I will investigate a particular instance of syntactic variation of the first type, namely the word order alternation that is possible for a large group of multi-word verbs in English. Consider (1). (1) a. Fred picked up the book, b. Fred picked the book up.

In (1), the alternation takes place within a verb phrase consisting of at least: • a verb (transitive or intransitive); • a morphologically invariant word, which will be referred to as a particle; • a direct object noun phrase. First of all, we need to distinguish the constructions in (1) from a superficially similar type of construction, exemplified in (2).

2

MULTIFACTORIAI, ANALYSIS IX CORPUS LINGUISTICS (2) Fred went into the forest.

Closer inspection reveals that (la) and (2) are, in spite of their superficial similarity, in fact quite dissimilar. First, and most importantly for the present work, (2) does not allow the word order alternation this study seeks to explain: (3) *Fred went the forest into.

Second, if the direct object is unstressed and pronominal, then the sentences of the type in (1) require the direct object to be positioned immediately after the verb (cf. (4)) whereas the sentences of the type in (2) require a VP-fmal position of such direct objects (cf. (5)). (4) a. b. (5) a. b.

*Fred picked up it. Fred picked it up. Fred went into it. *Fred went it into.

Third, sentence-final particles in sentences of the first type are generallystressed (cf. (6)) sentence-final particles in sentences of the second type normally bear no stress (cf. (7)). (6) a. b. (7) a. b.

What did Fred pick up?2 ??What did Fred pick up? ??What did Fred go INTO? What did Fred go into?

Finally particles of the kind in (1) must not be positioned sentence-initially for question formation (cf. (8)) while this is possible for particles of the kind in(2)(cf.(9)): (8) *L"p what did Fred pick? (9) Into what did Fred go?

The substantial differences between the constructions in (la) and (2) have been taken as indicative of the fact that what we have so far called particle does in fact not constitute a single word class - rather, it has been suggested that the particle in the sentence type in (1) is an adverb whereas the particle in (2) is a preposition.' Correspondingly, what we have informally termed multi-word verbs so far is not taken to be a homogenous class either: verbs in sentences of the type in (1) (where the particle is an adverb) arc commonly referred to as transitive phrasal verbs (TPVs) whereas verbs in sentences of the type in (2) (where the particle is a preposition) are commonly referred to as intransitive prepositional verbs.4 The main focus in the present work, however, is not to devise any new classificatory schemes or constructional tests such as those discussed above --

INTRODUCTION

3

I will focus on the variables that govern the word order alternation in (1). which has (even outside the Transformational-Generative paradigm) frequently been referred to as Particle Movement. In this study, 1 will use the theoretically more neutral term particle placement since (i) the theoretical foundations of the numerous stages of Transformational-Generative Grammar do not correspond to those of the present work (cf. section 1.3 below), and (ii) even within the Transformational-Generative paradigm, there is still considerable disagreement as to which element is being moved (particle or object or even both) and which of the two structures is basic or derived (cf., e.g., Legum 1968). Somewiiat surprisingly, in more than 100 years of literature on particle placement no generally accepted names for the two possible constructions have been coined. The word order where the particle is positioned directly next to the verb will be referred to as constmctionf;, the word order where the particle follows the direct object will be referred to as construction,.' (10) a. Fred picked up the book. = construction,, b. Fred picked the book up. = construction, As a cover term for both constructions, I will use the term verb-particle construction (VPC). Before we continue by outlining the theoretical assumptions on which this study is based, it is worth briefly discussing the history of TPVs in order to provide at least a cursory glance at the development of the word order alternation under consideration.'" 1.2 The diachronic development of phrasal verbs In Old English (OK), the form of TPVs was different from that of presentday Modern English (ModE). Originally, what arc now transitive phrasal verbs and intransitive prepositional verbs were characterized in OE by prefixation of the particle to the verb in the vast majority of cases: for instance, the ModE verb to go out was utgan in OE.' In an early stage of OE (as represented by early law codes dated c.600). the particle could not be separated from the verb (in this respect, this early stage of OE is similar to, say. fourth-century Gothic and Latin) and. according to Milliard (quoted in von Schon 1977: 10), the meanings of these verbs were restricted to literal, i.e. spatial, senses. According to Shlukhtenko (1955: 112), the complex verbs with the prefixed particle commonly bore stress on the first syllable of the verbal root, emphasizing the 'semantic character' of the verb and rendering the remaining parts of the verb less prominent (both prosodically and semantically). The inflectional suffixes of OE tended to lose stress as they were positioned word-finally However, prefixes expressing spatial relations were invariably stressed and 'the stress of the spatial prefix was inevitably bound to enter into contradiction with the stress of the root'. Since this contradiction was fundamental, in both English and German, the bond

4

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

between the verb and the prefixed particle loosened in two stages, which, taken together, have been called the First Particle Shift: • in a first developmental step, prefixed particles with a spatial meaning gradually began to detach themselves from the root: the particle could be separated from the verb but could only occur before the verb stem of the complex verb; • in the second stage, the particle could be separated from the root and also follow the finite verb - however, particles could not follow infinite verb stems. After this First Particle Shift, the OE structure of separable complex verbs was similar to that of Modern German as is shown in the first two rows of Table 1:1. From then on the particle was in late OE prose more and more frequently positioned behind the verb (especially; of course, in main clauses since these have finite verbs - in dependent clauses, pre-verbal position was used for a longer period of time). However, the development did not stop with post-position of particles behind finite verb stems because the separation of prefixed particle and verb did not remove the contradiction of two necessarily stressed items immediately following one another. Thus, in a second step a transition from prefixation to postpositional dislocation of verbal prefixes took place so that only a few centuries later (c.900), the particles could also be positioned behind non-finite verb forms. This so-called Second Particle Shift occurred in English and Norse, but not in German or Dutch, which is why the structure of non-finite phrasal verb forms in ModE differs from both OE and that of the other related languages, cf. the fourth row in Table 1:1. This development involved some type of stylistic variation of the two possible arrangements (prc- and post-verbal) and correlated with several other contemporaneous changes: • the general decline of verbal prefixes (cf. Hiltunen 1983: 145); • the overall tendency of English to develop into an analytic language (cf. Hiltunen 1983: 144; Kiffer 1965: 36, 135; Martin 1990/2, 11); • the influence of Scandinavian languages; • the introduction of so-called Echo Particles (i.e. a free adverb supplementTable 1:1 Gomparison of particle positions in different language stages Language

Finiteforms (verb-second)

Non-finite forms

Old English Modern German Modern Dutch Modern English

he eode ut cr ging aus uitging he went out

utgan ausgehcn uitgaan to go out

INTRODUCTION

5

ing the semantic impact of the semanlically weakened prefixed particle; cf. von Schon 1977: 32, 230-1); • the requirement to position the heavily stressed adverbial sentence finally in order to achieve end-focus (cf. Curine 1931; Shlukhtcnko 1955). The two Particle Shifts were completed rapidly in early ME by c. 1200 to c. 1300 when the posl-vcrbal position of the particle prevailed (cf. Hiltunen 1983: 6; von Schon 1977: 228-31). About that time, the complex verbs (e.g. bringan up and giefan up) could also take on non-spatial meanings (cf. Hiltunen 1983: 223). Until and during Shakespeare's time, phrasal verbs were frequently used in both colloquial and written language. Then, during the period from Milton to Johnson, they suffered a partial eclipse, but. from 1800 onwards they were used more often and more creatively The earliest examples of particles not only immediately following the verb but also following material intervening between verb and particle (i.e. the first cases of the alternation investigated here) can be found in texts from Alfred the Great (+899) and Abbot /Elfric (f about 1020). In these cases, the particle was regularly a directional adverb, more specifically an adverb denoting a point of the compass. Later on (e.g. in Ancrene Riuile of 1240, reported by Kiffer 1965: 50), however, other particles could also occur clause-finally in less literal cases, but these cases were by far outnumbered by those where the particle followed the verb directly. 1.3 Theoretical assumptions This study incorporates a multitude of accounts of particle placement in the last hundred years. Apart from these specific works addressing the linguistic problem of particle placement. 1 also rely on a variety of frameworks as a basis for my methodology7 and analysis. Since the methodological approach to particle placement advocated in this study is radically different from most others (at least in some respect), it is necessary to illustrate the most essential assumptions this study relics on. I assume, following recent approaches within the framework of Cognitive Linguistics, that language is a cognitive faculty closely interacting with other cognitive faculties. By this I mean that language is not taken to constitute an arbitrary, autonomous and fully predictably rule-governed module; I would rather argue that language is based on the same cognitive and perceptual mechanisms that seem to underlie many cognitive capabilities. For instance, allocation of memory resources, categorization and attention are three among many processes that govern or constrain most cognitive processes of which language is just one. What is more, I take language to be shaped not only by a variety of these and other cognitive processes as such (in the sense that, e.g., our memory constrains the number of possible embeddings in a single sentence), but I am also convinced that linguistic structure is to a large degree influenced (though not fully determined) by interactional processes in which language figures as a means of communication (as discussed in

6

MULTIFACTORIAL ANALYSIS IN CORPUS UXGUISTICS

many approaches that may be loosely subsumed under the cover term Functionalism; cf. Croft 1995; LakofT 1991: 54-5; Nuyts 1995: 293). In this regard, I find plausible the claim of Construction Grammar that pragmatic information may be conventionally associated with a particular linguistic form, thereby contributing to the interpretation process of a given linguistic expression (cf. Goldberg 1995: 3-4, 67; Kay 1995: 171-2; Langacker 1987: 20). This view of language as one among other cognitive processes has important methodological consequences for the present work: first, the present study allows for cognitively/psychologically real notions such as memory limitations, attention allocation, activation levels and prototype effects, to name but a few, as explanatory parameters. More specifically, I follow LakofPs (1991: 54) Cognitive Commitment, i.e. the 'commitment to make one's account of human language accord with what is generally known about the mind and brain from disciplines other than linguistics'. This entails that a description and explanation of linguistic phenomena with reference to cognitively real notions as in the present work must: 1 incorporate a multitude of independent variables rather than relying on monocausal explanations in order to provide an adequate account of these phenomena; 2 allow for a certain degree of inexplicable variance in the data depending to some extent on, e.g., the subjective construals (in the sense of Langacker 1987: 138-41) of scenes by human conceptualizers and speakers. What is more, many of the cognitive processes argued to be relevant are inherently scalar in nature, which is why descriptions and explanations in probabilistic terms and with reference to non-Aristotelian (i.e. non-clear cut) categories will be the norm rather than the exception.8 The above-mentioned consequences also have important ramifications concerning the nature of the linguistic data on which my analysis is based. Many approaches base their analyses on intuitive and introspective data (such as inventing sentences or judging their grammaticality or acceptability). This study, however, is exclusively based on naturally occurring data from a large corpus (the British National Corpus) in order to guarantee (i) that the results are based on how speakers actually put language to use and (ii) that generally acknowledged standards of scientific research such as objectivity, reliability and validity are properly met. In addition, it will soon become obvious that the multitude of variables that needs to be considered in combination with the amount of data used render it impossible to handle the mass of data without fairly sophisticated statistical procedures; while human native speakers subconsciously somehow manage to keep track of all variables in real time, because of the cognitive restrictions just mentioned above, a human analyst simply cannot cope with all the information of twenty or even more variables both simultaneously and objectively without such techniques.

INTRODUCTION

7

The overall approach of my analysis to particle placement will adopt a psycholinguistic perspective; more specifically, I am going to investigate particle placement from the perspective of online speech production. Psycholinguistic models to which my analysis will refer are the following: 1 Processing-based accounts of constituent ordering such as those by Hawkins (1991. 1994), where the resolution of word order choices is largely dependent on the processing effort associated with the different structural options. 2 Interactive activation models (lAMs) of the sort proposed by, e.g., Dell (1986). 3 The functionally oriented Competition Model by Bates and MacWhinney (1982, 1989). A more detailed explanation of these models and their relation to particle placement will be outlined below. 1.4 Outline of the study

On a large scale, this study is organized in the same way as most empirical studies: introduction, methods, results and discussion. Chapters 2, 3 and 4 are introductory in the sense that they provide essential background information on which the study rests, main hypotheses and terminology; Chapter 5 is concerned with the data that were analysed and the methods that were put to use; Chapters 6 and 7 present the results of the study and discuss their, in some respects, far-ranging implications; Chapter 8 provides a different perspective on particle placement and complements the previous discussion. Chapter 9 provides a conclusion. Let us look at the organization of this study in more detail. Chapter 2 discusses previous approaches to particle placement. The focus will be on every single variable that has been argued to govern particle placement. In the course of this discussion, the theoretical orientation of the analyses will be considered, but will only be topicalized to the extent that it contributes to our understanding of how this variable influences particle placement.9 The discussion is organized into levels of linguistic research: section 2.1 deals with phonological variables; section 2.2 investigates morphosyntactic variables: section 2.3 examines some semantic variables; sections 2.4 and 2.5 respectively deal with several discoursefunctional variables and some other, not so easily classifiable, variables. Each of the variables will be exemplified in some detail as the understanding oi every single variable is crucial to comprehending the course of analysis to be pursued in later chapters. Finally, section 2.6 summarizes and critically evaluates the (inventory of) variables proposed so far and discusses in detail several methodological shortcomings of previous analyses (most of which pertain to analyses of other cases of syntactic variation as well).

8

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

Chapter 3 discusses the main objectives (both linguistic and methodological in nature) of this work in detail. Suffice it here to mention that the linguistic goals of this study are to describe, explain and predict particle placement — the methodological goal is to show that syntactic variation should be investigated by advanced statistical techniques that have hitherto only rarely been used for cognitive-functional analyses of syntactic variation but are much more frequently used in contemporary corpus linguistics, psycholinguistics and other scientific disciplines.10 Chapter 4 proposes a hypothesis, the so-called Processing Hypothesis, in order to explain the word order alternation at hand. This hypothesis explains the choice of construction by native speakers in terms of the amount of processing cost associated with the two constructions, thereby subsuming most of the previous variables under a single notion and excluding some variables from further consideration. Although my analysis aims at being more comprehensive than previous analyses, it follows the tradition of works by Givon and Hawkins. Chapter 5 is devoted to methodological issues. The present work is completely corpus based, which is why 1 start by illustrating exactly how the corpus data came to be selected and how they were analysed with respect to the afore-mentioned inventory of variables. For each variable, naturally occurring sentences from the corpus data will be used to exemplify the assignment of values of interval/ordinal variables arid of levels of categorical/nominal variables to the variables figuring in the statistical evaluation to follow. Chapter 6 presents and discusses the results. First, in section 6.1, the most basic (i.e. monofactorial) descriptive results (correlation coefficients and cross-tabulation) arc reviewed thoroughly in order to find out which variables govern the alternation when each variable is examined in isolation. Put differently, we are concerned with the absolute strength of each variable. Second, section 6.2 investigates oppositions between variables and values/levels of variables in order to illuminate the interrelationships between variables, i.e. their respective strengths compared to one another. In other words, here, the relative strengths of variables and values/levels are dealt with. Lastly, in section 6.3, a multifactorial analysis is discussed by means of which we can (i) assess the current state of the art by answering the question 'to what extent can the alternation be explained?', (ii) shed light on the way the constructional alternation can be explained given a particular complex speech situation and (iii) predict native speakers' choices in authentic discourse situations. Although the advent of corpus linguistics and the rapid growth of other approaches such as psycholinguistics and/or computational linguistics have increased the statistical awareness of linguists, some of the statistical procedures used below are quite advanced. I therefore devote some space in each section of Chapter 6 to a brief explanation of these procedures. Chapter 7 is devoted to the discussion of some important implications pertaining to more general linguistic questions. Section 7.1 shows how

INTRODUCTION

9

prototypical instances ol both constructions can be identified objectively on an empirical basis. On the other hand, T also address the question of whether a prototypical VPC (comprising both word orders) can be determined. Section 7.2 discusses how the present analysis relates to other conceptually similar analyses of variability in language (though not necessarily syntactic variation) and their importance for linguistic theories. Lastly, section 7.3 addresses the question of how my analysis relates to the conflict between syntactic and discourse-functional approaches to syntactic variation. T concentrate on comparing my analysis to another, superficially similar, analysis of particle placement in terms of processing cost (Hawkins 1991, 1994). Chapter 8 addresses a conceptual refinement or complementation of the preceding analysis in terms of processing ellbrt as embraced by Givon (1992b) and Hawkins (1994). I propose a means to abstract away from the processing notions introduced before in order to further integrate the present findings into recent psycholinguistic interactive activation models (lAMs) such as those proposed by McClelland and Rumelhart (1982), Rumclhart and McClelland (1981), Stcmberger (1985), Dell (1986) and Bates and MacWhinney (1989). It will be shown how they go beyond a processing approach and, ultimately, seem to be the best alternative. Chapter 9 concludes the present study: I summarize the basic findings and provide an outlook on future research. Finally, Chapters 10 and 11 contain various appendices with tables and figures of factors, registerdependent results, a list of TPVs and the list of references. Notes 1 Note that, according to the above definition of allo-scntences, this study is restricted to truth-coriditionally equivalent sentence pairs - I will not consider sentence pairs where the word order differs in their literal meaning, e.g. The wind blew down the chimney and The wind bleif the chimney down. 2 Throughout this study, capitalization in example sentences is used to mark stressed constituents. 3 Cf. O'Dowd (1998: section 2.1) for a comprehensive discussion of a variety of preposition vs. (adverbial) particle tesls, which arc summed up as follows: The1 general principle underlying these tests is that in certain vcrb-P sequences, P constructs closely with the verb, performing an adverbial function while in others it constructs with the following NP. performing a prepositional function. (O'Dowd 1998: 14-15) 4 According to Bolinger (4971: 3), this classification appears to be the one most generally accepted; cf. among others Mitchell (1958: 106); Palmer (1988: 219ff.); Quirk et al. (1985: 1152—63). However, there exists considerable terminological profusion concerning the verb class under consideration; other terminological options are: VVortverband (Garstensen 1964), verb-particle colligation (Charlton 1990), separable verbs (Francis 1958), verb-particle construction (Fraser 1965. 1966, 1971, 1976), verb-adverb compound (Kennedy 1920), compound verbs

10

5

6 7

8

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS (Kruisiriga and Erades 1953). discontinuous verbs (Live 1965), verb-adverb locution (Roberts 1936) and two-word-verbs (Taba 1964). It is also worth noting that sometimes (e.g. Cowie and Mackin 1975, 1993: ix) the term TPV is restricted only to those TPV constructions where the meaning is idiomatic (as in, e.g., He gave up smoking as opposed to those instances where the meaning is literal (such as He brought the book back), although (i) it is widely accepted that there is no clear-cut boundary between idiomatic and literal meanings, and (ii) there is also disagreement as to how intermediate stages on a continuum between idiomatic ity and literalness can be objectively measured. Apart from that, there is yet another similar class of sentences, namely those exemplified by He painted the house green or He talked himself unconscious. These constructions have sometimes been considered equivalent to TPVs (cf. Bolinger 1971: 67 71) and are referred to as verb-adjective constructions (Quirk el al. 1985: 1167-8). Goldberg (1995: Chapter 3, 7-8) treats them as an extension of the socalled caused-motion construction, namely rcsultativc constructions. Although these constructions are not of major interest here, we will briefly return to them in Chapter 4. It would, of course, be much nicer to have more mnemonic terms for the two constructions, which is why one could simply coin terms such as continuous construction and discontinuous construction for construction,, and construction, respectively. However, the terminological option of construction,, and construction, has one very important advantage: the numbers 0 and 1 have been used in the statistical calculations in the chapters to follow, and the interpretation of correlation coefficients makes it necessary to know which construction is characterized by which value; in other words, even if I used continuous construction and discontinuous construction for the introductory parts of this study, the reader would have to know which construction is encoded by 0 and which by 1 for Chapters 6 and 7 anyway. Therefore, the terms construction,, and construction, will be used throughout this study. (Note that the assignment of the values does not have anydeeper conceptual import and could just as well have been the other way round. As a mnemonic help, note that the subscript in the construction's name provides the number of constituents between the verb and the particle.) We will briefly return to diachronic aspects of VPCs in section 7.1 below. This pattern is still present in only a limited number of ModE verbs with either a literal (e.g. to upraise and to uplift) or a more idiomatic meaning (i.e. one where the usual spatial meaning of the particle is not fully present in the meaning of the verb such as to forgive, to mislead and to understand). There arc even combinations of verb and particle that exist in both combinations in ModE, but the two combinations have different meanings: to overtake does not mean the same as to take over. As early as 1917, Deutschbein argued for a way of analysis that is in this respect fully compatible with the present approach and does not bear the weaknesses of many linguistic accounts to be discussed below: owing to their being couched in terms of necessary and sufficient conditions they were quite inflexible and remote from 'natural' data and their interpretation with respect to natural explanatory parameters of language (cf. Givon 1979: 3-5): Dabei hahe ich. vvie ublich, die Spracherscheimmgeii zu formulieren gesucht, doch mochtc ich ausdriicklich hervorheben. dafi cs sich menials urn Gesetze irn slrengsten Sinne handelt, sondern ebcn nur um Krafte, die nach einer bestimmtcn Richtung wirken. (Deutschbein 1917: vii)

INTRODUCTION

11

9 For instance, given the theoretical assumptions of this study, it would be pointless to review and discuss in detail the question of whether particle placement is better analysed by incorporation due to the Case Adjacency Principle (Stowell 1981: 113) or the Small Clause Analysis by Kayne (1985: 129). 10 A more detailed discussion of my objectives is postponed until Chapter 3 as most of them follow from my characterization of linguistic and methodological drawbacks of previous analyses.

2

Review of literature

Particle placement is a linguistic phenomenon that has been noticed in many traditional grammars and a variety of linguistic frameworks for a very long time. One of the very first mentions of this alternation can be traced back to an English Grammar from 1712 (Mattaire 1972). However, the first linguistic investigations that go beyond simply noticing the different possible word orders in a prescriptive fashion date back to 1892 (Sweet 1892). In other words, particle placement has now been investigated for about 100 years. The following discussion of the previous findings will focus on the variables that have in these analyses been argued to influence the constructional alternation. For each sub-branch of linguistics, I will name the respective variables and illustrate which of their values/levels are purported to influence particle placement in which way.' As was mentioned in section 1.4, it is important to note that I will not discuss the theoretical underpinning of the previous proposals in more detail than is necessary for the complete understanding of the variables' impact on the constructional alternation. This also implies that not all of the approaches that have argued for a particular variable's influence can or should be named here: it does not make sense to name repeatedly dozens of references to analyses postulating perhaps a single variable. This entails that, to name at least one example, no exhaustive theoretical comparison of the claims of the various schools that emerged from the Transformational-Generative paradigm will be provided since nearly all of the dozens of these approaches centre on the same two variables." After the discussion of variables in sections 2.1 to 2.5, section 2.6 summarizes and evaluates the previous analyses and points out several recurrent shortcomings. 2.1 Phonological variables

The most important phonological variable thai has been argued to influence particle placement is concerned with the stress pattern of the verb phrase.3 As wras already noticed in even the earliest works, if the direct object is stressed, then construction,, is much more likely to occur (cf.. e.g., Van Dongen 1919: 352; Kruisinga and Erades 1953: 78; Bolinger 1971:

RKV1KW OK UTKKATl'RE

1:5

50-5). Consider (11), where the stress on the direct object serves a contrastivc function. (11) a. Fred picked up the BROWN BOOK, not. the blue one. b. Fred picked the BROWN BOOK up, not the blue one.

This variable is, of course, intimately related to both semantic and discourse-functional aspects of the utterance, which is why we will return to it again below.' Apart from the stress pattern of the verb phrase, there is a second phonological variable that has been put forward in one analysis, namely the phonetic shape of the verb. According to Frascr (1974: 571), verbs not bearing initial stress prefer construction,; this means that, for instance, the TPV to divide up should be less acceptable in (12a) than in (1 2b). (12) a. Fred divided up the cake, b. Fred divided the cake up.

2.2 Morphosyntactic variables Many of the variables that have been proposed are morphosyntactic in nature. A variable that has been mentioned in every single analysis (e.g. Van Dongen 1919: 351-2; Kennedy 1920: 30; Chomsky" 1957: 75-6; Rohrbacher 1994: 194-5) is concerned with the NP type of the direct object of the TPV: if the direct object is pronominal as in (13) rather than lexical, then construction! is, at least in general, obligatory. (13) a. *Fred picked up it. b. Fred picked it up.

The only exception to this rule is the class of contrastively stressed pronominal direct objects (cf. Fraser 1976: 20), a combination that is very rare and is very likely to occur only with further modification of the utterance, as is exemplified in (14). (14) a. Fred picked up HIM, not her. b. PFred picked HIM up, not her.

Tn some approaches, a distinction has not only been made between pronominal and lexical direct objects — according to, among others, Van Dongen (1919: 352), Kruisinga and Erades (1953: 77-8) and Quirk et al. (1985: 1370), there is a somewhat intermediate class of direct objects, namely refercntially vague semi-pronominal nouns such as matters or things, which also have a strong preference lor construction,. (15) a. ?Thcy talked over matters, b. They talked matters over.

14

MULT1FACTORIAL ANALYSIS IN CORPUS LINGUISTICS

For lexical objects, it has been claimed that the determiner of the direct object also plays a role in the choice of one construction over the other (Chen 1986: 84'on the basis of Givon 1983; Gries 1999): indefinite determiners tend to occur with construction,, and definite determiners tend to occur in construction,.1 However, this claim cannot be exemplified simply on the basis of acceptability judgements since minimal pairs of sentences with TPVs differing only in the determiner of the direct object do not differ markedly in acceptability: (16) a. b. c. d.

Fred Fred Fred Fred

picked picked picked picked

up a book. a book up. up the book. the book up.

(16a) and (16d) are argued to be more acceptable or likely to occur than (16b)and(16c). Another influential variable is concerned with the length of the direct object: in nearly all studies, it is argued that long direct objects lead to a preference for construction^ Consider (17), where the long direct object not only prefers, but seems to require, construction,,. (17) a. Fred picked up the book John had bought him while he was in Europe, b. *Fred picked the book John had bought him while he was in Europe up.

This variable has come in several guises: in most studies, it is not even mentioned how length is measured. Some studies measure the direct object's length using the number of syllables (e.g. Chen 1986; Gries 1999);6 some other studies rely on the number of words as an indicator of length (e.g. Hawkins 1991, 1994).7 Several other scholars have suggested that it is not so much the length of the direct object that is relevant for particle placement but rather the complexity of the direct object (cf, e.g., Eraser 1966: 46, 1976: 20; Ross 1986 [1967]: 32-3; Yeagle 1983: 11-12; Kayne 1985: 106). In general, one might argue that the length of the direct object and its complexity (however that is to be quantified) will be highly correlated (recall Hawkins's way to operadonalize complexity by number of words) - however, Chomsky (1961: n. 18) and Fraser (1966: 9 n. 3) have argued convincingly that there are cases where the influence of length is different from that of complexity. Consider (18) (Fraser's example and acceptability judgements). (18) a. The student worked more than seven of the difficult examples out. b. *Thc student worked the example which he recognized out.

While in (18a) the direct object is longer (7 words, 12 syllables) than that in (18b) (5 words, 9 syllables), (18a) is still more acceptable than (18b) (at least if we follow Fraser's intuitive assessment):8 in i'18a), the direct object noun phrase is very long, but not as complex as the one in (18b), where the

REVIEW OF LITERATURE

15

direct object noun phrase contains an embedded clause. Similar evidence was gathered by Hunter (1981), who concluded that the direct object's complexity is in fact more important than its length, lending further support to Fraser's claim. Be that as it may, even in view of Hunter's evidence and Eraser's intuitions, it is not obvious how complexity can be measured reliably (Fraser 1976: 20): cf., however, Wasovv (1997b). who demonstrates that many measures of grammatical weight yield similar results. 2.3 Semantic variables The first semantic variable to be discussed is concerned with idiomatic meanings of the verb phrases in which particle placement occurs: following, say, Fraser (1974: 573) and Chen (1986: 82), if the meaning of the verb phrase is idiomatic,'1 then construction,, is preferred. (19) a. b. (20) a. b. (21) a. b. (22) a. b.

Fred has tried to eke out a living, *Fred has tried to eke a living out. Fred brought down the plane, Fred brought the plane clown. I will turn over a new leaf. (Potter's 1965: 287 example) I will turn a new leaf over. He threw up his dinner because he got food poisoning. (Fraser 1974: 573) He threw his dinner up because he wanted to stain the ceiling.

In (19a), the meaning of the verb phrase is idiomatic and construction,, is acceptable in (19b), however, the idiomatic verb phrase does not go together with construction, and (19b) is ungrammatical. In (20a), the verb phrase is ambiguous in that it can cither mean that Fred had a (toy) plane and brought it to some lower location (the literal meaning) or that Fred is a soldier who has shot down a fighter plane (the idiomatic meaning). On the other hand. (20b) licenses the literal interpretation only. According to Potter, (21 a) is ambiguous between / will turn over a new page of the book and / will begin a new life, but this ambiguity is not present for (21b), where again only the more literal interpretation is possible. Finally, for (22a), Fraser has claimed that, given the idiomatic meaning of the TPV (note the subordinate clause), the choice of construction,, is most natural; for (22b), on the other hand, the literal meaning of the TPV7 results in a preference for construction, — if each main clause was to be joined with the subordinate clause of the other main clause, awkward sentences would result. One has to bear in mind, however, that this constraint is far from being absolute for two reasons. First, the idiomaticity of the verb phrase need not. have the above-mentioned effect at all (cf. Cowic and Mackin 1993: ix): although she made up her face is clearly idiomatic (as opposed to, say; Bill carried away the rubbish'), both sentences allow for a change to construction, - she made her face up and Bill carried the rubbish away are fully acceptable (especially given specific discourse contexts); moreover, she made it up even requires

16

MUI.TIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

construction j. Second, the meaning of a verb phrase cannot always be categorized as being either fully idiomatic or totally literal (cf. also Gibbs 1994: chs. 5, 6). Rather, there are many cases where the meaning is somewhere between these two extremes. For instance, in it has taken many years to bring the town up to the standard the meaning is definitely not literal since the town has not been moved to a spatially higher position, but equally the meaning is definitely not fully idiomatic as it can be easily computed on the basis of what Lakoff and Johnson (1980: 14) have called oricntational metaphors (here, GOOD is UP); that is, there seems to be an intermediate level of meaning between idiomatic and literal, namely metaphorical.10 Another semantic variable concerned with the meaning of the verb phrase that has been brought up by Fraser (1976: 20-1) is the habitual meaning of the verb phrase: if the verb phrase denotes the habitual meaning, then construction,, is preferred; (23) is his example. (23) a. b. c. d.

The police The police The police The police

are tracking down criminals. arc tracking criminals down. track down criminals. track criminals down.

According to Fraser, (23a) and (23c) are more natural than (23b) and (23d) respectively. An additional variable Fraser (1974: 573) has argued for is concerned with semantic modification of the particle: 'Several particles, notably up, can take a perfective marker such as all, all the way or completely or an aspectual marker such as right. When such a marker is present, the particle must follow the object.' (24) a. b. (25) a. b.

*I will clean all/right up the room, I will clean the room all/right up. *Harry said he would bring all/right over the vegetables, Harry said he would bring the vegetables all/right over.

As an additional variable, I have suggested in a former analysis (Gries 1999) that the degree of cognitive entrenchment (or cognitive familiarity) of the referent of the direct object contributes to particle placement. The degree of entrenchment has, following analyses by Deane (1987. 1992: 199-236), been measured by the position of the direct object's referent on the entrenchment hierarchy in Table 2:1 (based on an adaptation of the Silverstein Hierarchy). On the basis of a corpus analysis and a survey of acceptability judgements of native speakers of British English, T have shown that for frequency counts and acceptability' judgements highly entrenched referents of direct objects correlate significantly with construction, while barely entrenched referents of direct objects correlate significantly with construction,). The last semantic variable to be mentioned is concerned with the semantic focus of the verb phrase. Both Bolinger (1971) and Yeaglc

REVIEW OF LITERATURE

17

Table 2:1 Entrenchment hierarchy of dries (1999: 122-4), based on Deane (1987, 1992) Least entrenched

1 2

3 4 5 6 7 8 9 10 11

Abstract entities Sensual entities Locations Containers Concrete objects Animate beings (other than humans) Kin terms Proper names 3"' person singular pronoun 2'"' person singular pronoun 1 M person singular pronoun Most entrenched

(1983) have argued that the two possible constructions denote the same objective situation (i.e. the two constructions arc, apart from cases of idiomatic meanings discussed above, truth-conditionally equivalent), but the position of the particle tends to highlight one out of two possible readings: when the particle is postposed. it tends to modify the noun; when it stands next to the verb, it behaves more like a verbal affix (cf. Bolinger 1971: 82). More specifically, Bolinger has argued that speakers use the coupling of accent and the position of the particle to highlight what is important about the utterance: accenting an item and/or its sentence-final position serve to indicate that the referent of the linguistic expression corresponds to the semantic peak of the utterance whereas de-accenting an Hem and/or its postverbal position marks it as being redundant (Bolinger 1971:58-9)." Ycaglc's (1983) analysis has solely concentrated on scmantico-cognitive motivations for the two word orders. As a starting point. Yeagle (1983: 1245) has, following Lindner's (1981) classic analysis of out and up, advocated an analysis within the framework of Cognitive Grammar where she convincingly shows that the above-mentioned distinction of whether the particle is an adverb or a preposition is fundamentally flawed in that it forces us to conclude that up belongs to two different parts of speech: we can classify up as an adverb in some cases (cf (26)) and as a preposition in others (cf. (27)). (26) a. b. (27) a. b.

Fred ran up [N!, the flag] . Free! ran [Nl, the flag] up. Fred ran [,>,, up [Nl, the hill]], *Fred ran [Xl, the hill] up.

18

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

Moreover, she also observes a more general semantico-cognitive regularity that neatly accounts for this distributional pattern: particles must not follow their landmark. In (26), the flag designates the trajector of up and the landmark of up is not encoded (up profiles the relationship of some primary/ focal participant, namely the flag, to some, in this case unexpressed landmark, probably something like a pole}: both word orders are possible without violating Yeagle's rule. In (27), however, Fred denotes the trajector of up while the hill denotes the landmark of up and (27b) is ruled out since the particle follows its landmark.12 Yeagle's analysis of particle placement is derived from her analysis of the word class of particles. According to her, construction,, is used in order to highlight the unified view of the action and its end as continuous construction, is used in order to highlight the resultant state (designated by the particle) of the referent of the direct object. Unfortunately, no independent evidence is offered to support these claims. 2.4 Discourse-functional variables

So far, our main focus has been on variables determining the final phonological and morphosyntactic shape of VPGs and variables determining their sense. However, in the preceding section, it was already shown that, following Bolinger (1971), the context of an utterance can influence the choice of a construction by a native speaker as well. Among very first accounts in which the context of the utterance in question has been considered are works by Kruisiriga and Erades (1953) and Erades (1961), who proposed that the news value of the direct object motivates the choice of construction. Consider (28). (28) a. ?Wc'll make up a parcel for them . . . On the morning of Christmas Eve together we made up the parcel. b. We'll make up a pareel for them . . . On the morning of Christmas Eve together we made the parcel up.

In (28), where the referent of the direct object is introduced in the first sentence, the panel is not newsworthy in the second sentence, where the TPV is used. Thus, it is construction, that is preferred (i.e. (28b)) while construction,, in (28a) sounds rather odd. One empirical investigation of the role of news value has been done by Bock (1977). In a question-answering task on particle placement and nine other cases of syntactic variation, Bock found that construction, was preferred by speakers when the referent of the direct object was mentioned in the question whereas construction,, was more often used when the direct object's referent had not been mentioned previously. A second study on the role of news value or givenness of the direct object's referent is Peters (1999, 2001) using a picture-description sentence completion task. Subjects were asked to complete sentences following a stimulus sentence and a schematic

REVIEW OF LITERATURE

19

black line drawing; the independent variable was whether the referent of the direct object to be produced by the subjects (given the drawing) was mentioned in the stimulus sentence (given) or not (new) the dependent variable was the choice of construction. Two of Pcters's findings are relevant to our analysis: first, there is a highly significant overall preference for construction,, (both in the control condition LX2(1) = 6.942; p = .008] and, taken together, in the two experimental conditions [/ (1) = 23.7; p">

the above argument based on end-focus and the resulting spatial causedmotion interpretation. With this account of literal and idiomatic VPCs, we can also explain a phenomenon that has so far only been observed by some scholars but that has never been explained, namely the fact that, for sentences which could in principle be interpreted both literally and idiomatically, the literal interpretation is preferred for construction, and the idiomatic interpretation is preferred for construction,) as in (22). here repeated for case of reference as (45). (45) a. He threw up his dinner because he got food poisoning. (Fraser 1974: 573) b. He threw his dinner up because he wanted to stain the ceiling.

In (45a), an idiomatic meaning can be processed easily since the verb and the particle are adjacent; the literal meaning is in principle also possible, but is ruled out in this example by the subsequent subordinate clause. In (45b), by contrast, the main clause focuses on the spatial (and resultative) meaning of the sentence-final particle (along the lines argued above) so the literal meaning is the most natural interpretation; an idiomatic meaning is, however, not licensed as the two components of the TPV are too distant from each other, given their strong semantic interdependence. In this example, the literal meaning is further supported by the message of the subsequent subordinate clause that is concerned with upward movement. The next semantic variable to be related to the Processing Hypothesis is semantic modification of the particle. According to Fraser, only construction, allows for perfective or aspectual modification. This observation can be related to the fact (observed by Dirveri and Raddcn 1977: 184-6) that this perfective or aspectual modification is only possible for the literal sense of particles (which, for reasons just discussed at length, in turn prefer construction,), whereas the perfective or aspectual modification is not possible for idiomatic verb phrases requiring construction,,. Thus, Frascr's observation results from a more general principle, which, in turn, is related to Dirvcn and Raclden's observation. In other words, this variable does not seem to have a direct causal influence on particle placement rather, it is correlated with another variable (namely the idiomaticity of the verb phrase or, more precisely, the lack of it) that has a direct influence. Be that as it may, the prediction following from the variable conforms to the Processing Hypothesis. Lastly, we are left with the animacy of the referent of the direct object. This variable was included in the remainder of the analysis because it is one among others that has empirically been found to be relevant for replacing the entrenchment hierarchy as discussed above (cf. section 2.6). Bock's (1982: 17 23) discussion of variables concerned with lexical retrievability referred to above also mentions that animacy of referents contributes to constituent ordering. However, on the basis of the Processing Hypothesis, no significant effect of animacy of the referent of the direct object on particle placement is to be expected: there is no reason to assume that animate

56

MUI.TII'ACTORIAL ANALYSIS IN CORPUS LINGUISTICS

referents yield context-dependent processing requirements substantially different from inanimate referents, and there is also no reason to assume that animate referents are more likely to undergo a literal change of location (due to caused motion) or a change of state; in fact, animate referents are more likely to be found as subjects of the VPCs, i.e. as causers or agents, rather than as the (typically inanimate) objects of the actions denoted by TPVs. Therefore, the variable will be empirically investigated, but is hypothesized not to contribute significantly to particle placement (cf. also Browman 1986 and especially McDonald et at. 1993). To summarize, we have seen that apart from several discourse-functional determinants of processing effort, a variety of interrelated semantic variables argued to influence particle placement is also strongly connected to processing effort: the notion of end-focus, which ultimately serves the purpose of facilitating the processing (of the most important aspects) of messages, is responsible for slightly different semantic interpretations of the utterance (depending on which element is positioned in the focal position of the clause). Finally, the degree of idiomaticily of the VPC and other variables related to it are also influenced by processing cost. The following section will deal with morphosyntactic characteristics of the two constructions and their relation to processing. 4.3 Morphosyntactic variables

In order to describe the relationship between the morphosyntactic characteristics of VPCs and processing effort, we need to look at two different morphosyntactic elements figuring in these constructions: the complex verb and the direct object noun phrase. Let us first turn to the complex verb. It has been argued elsewhere (Hawkins 1994; Rohdenburg 1996: 150; Cries 1999), that construction,, simplifies structural processing (i) for the speaker in that the particle immediately follows the verb and, thus, does not have to be borne in mind until the direct object has been uttered and (ii) for the hearer in that he processes the verb and the particle before the- direct object is also processed. In construction i, by contrast, the speaker, while producing the utterance, has to bear in mind that there is still a particular particle to be inserted after the direct object has been completely uttered and the hearer has to wait longer for assigning the correct parse to the incoming expression, namely until some yet unknown particle completes (and sometimes even disambiguates) the verb.12 But what about the direct objects of TPVs? In order to facilitate communication, language has several means of guiding the hearer's interpretation of the incoming message. These means have a variety of explicit linguistic manifestations with respect to the encoding of the entities referred to by the speaker. In section 4.1, we have seen how referents of linguistic expressions can differ with regard to their degrees of identifiability and activation. In English, the idcntifiability status of a concept used in an utterance is connected to issues of definiteness,

KEY NOTIONS AND HYl'OTHKSKS

57

pronominalization and the syntax of reference - the degree of activation, on the other' hand, is connected to issues of voice, word order and sentence prosody (cf. Tomlin 1986: 39-40). Given these morphosyntactic correlates (one might even say, manifestations) of identifiability and activation (and, thus, processing), it conies as no surprise that the morphosyntactic variables influencing particle placement can be related straightforwardly to the processing cost of the utterance. Let us start with the variable NP type of the direct object. This variable is in fact quite closely connected to utterance processing: personal pronouns and semi-pronominal refcrentially vague nouns are only used when their referents arc identifiable and active whereas lexical noun phrases are much more likely to be used with unused referents and are probably always used for brand-new referents. Again, the distribution is the same as predicted above in (44): the active referents of personal pronouns do not require much processing effort and correlate with construction, - referents of lexical noun phrase objects arc, on the whole, more likely to require more attention and occur preferably in construction,,. Likewise, the determiner of the direct object noun phrase is also concerned with processing aspects: speakers do not decide randomly in favour of definite or indefinite determiners - instead, one can find a fairly clear pattern as summarized in the following quote: Linguists traditionally deal with the binary distinction between definite and indefinite, with the former marking topics which the speaker assumes the hearer can identify uniquely, is familiar with, are within his file (or register) and thus available for quick retrieval. On the other hand, indefinites are presumably topics introduced by the speaker for the first time, with which the hearer is not familiar, which therefore are not available to the hearer readily in his file. (Givon 1983:9-10) Comment is hardly called for: definite determiners (used for active referents barely requiring extra attention arid processing) are said to prevail in construction | and indefinite determiners (used for unused or even brand-new referents requiring conscious activation) are said to prevail in construction,, so that the pattern found matches the expected distribution. Length of the direct object and complexity of the direct object (irrespective of how these are measured; cf. section 5.2 lor mailers of operationalization) can be dealt with simultaneously. Self-evidently, longer and more complex noun phrases require more processing effort just because of their heaviness and likely complexity while shorter (and commonly simpler) noun phrases can be more easily processed. But apart from this purely structurally motivated approach, there is also a functional principle at work: 'the new information often needs to be stated more fully than the given (that is, with a longer, "heavier" structure)' (Quirk et al. 1985: 1361). Thus, if the newness of a referent, on average, renders direct objects long and complex (in order to provide the information necessary for the hearer), the larger amount of linguistic material requires more processing effort than the one needed for given information less heavily encoded. Ultimately, both the structural

58

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

variables and their functional motivation go hand in hand and much processing effort is again linked to construction,, and little processing effort to construction,. 4.4 Phonological variables The variable stress pattern of the verb phrase, can be straightforwardly related to processing requirements: in functional analyses of information structure within English sentences, it is useful to distinguish two kinds of information, namely given and new information.13 It is common ground that stress on a linguistic expression typically serves to indicate the newness or importance of the referent of this linguistic expression. For the present purpose, it is not necessary to differentiate categorically between stress signalling newness and stress signalling importance - the crucial aspect here is that, by assigning stress to a linguistic expression, the speaker directs the attention of the hearer to the respective referent, thereby increasing the processing cost associated with that referent (cf. again n. 6). Since stress on the direct object yields a preference for construction,, while stress on the particle yields a preference for construction,, the assignment of stress to linguistic expressions correlates with the principle of end-focus. The elements that are intended to be processed more thoroughly are positioned in such a way that they can be accordingly processed, resulting in the distribution as predicted in (44): the expressions whose referents are hardest to process occur clausefinally. 4.5 Remaining variables

The next variable is concerned with the presence of a directional adverbial following either the verb or the particle. If a directional adverbial follows the VPC, then it typically serves to either elaborate the path along which the referent of the direct object is being moved (cf. (46a)) or the resultant location of the referent of the direct object (cf. (46b)). (46) a. So Tom took [NI, Peter] along [PP past the new Pump House]. b. Fred put [XI> the book] down [Pp on the table].

For construction,, where the spatial meaning is, according to the line of reasoning in section 4.2, foregrounded, it is therefore quite natural to expect additional material (in the form of a directional adverbial) providing additional information on the direction or the endpoint of the movement process; for construction,,, however, the opposite would be expected as construction,, does not in general denote a movement process that can be further elaborated with information concerning directionality (cf. above). In other words, the directional adverbial as such does not causally influence particle placement, but it is correlated with another variable that does, namely the degree of idiomaticity of the construction. This is what one

KEY NOTIONS AND HYPOTHESES

59

would expect since it is unlikely to assume that directional adverbials influence particle placement against the unidirectional flow of time; still, at least argumentatively, the distribution predicted in the Processing Hypothesis seems to be fully justified. However, there is a second way of relating this variable- to the Processing Hypothesis. Up to now the variable directional adverbial was taken to contribute to particle placement due to its semantic contribution to the meaning of the utterance. Equally possible would be the following effect: compare an analysis of sentences such as those in (46) along the lines of Hawkins's EIC principle or the principle of end-weight to that of the sentences in (47). (47) a. So Tom took Peter along. b. Fred put the book down.

Length of NP=1 Length of NP=2

Length of Farticlc=l Length of Particle=l

The EIC principle predicts that longer constituents will be positioned sentence-finally. In (47). the direct objects are short (one and two words respectively) and the particles are short (one word) so EIC or the principle of end-weight do not make strong predictions. In (46) (repeated with additional information as (48)), by contrast, the particles are followed by spatial prepositional phrases. In view of the just-mentioned semantic function of these spatial prepositional phrases, one could assume that the particle and the spatial PPs belong together closely in terms of constituency (just like prepositional phrases as in Fred took the glass //>r out of the cupboard]), yielding a semantic constituent we might informally call 'particle phrase' or PartP for short (for lack of a better term). 14 (48)

a.

So Tom took Peter [P.lrtP along [PI, past the new Pump House]]. NP=1 PartP=5 b. Fred put the book [ranP down [HJ, on the table]]. NP=2 PartP=4

Thus, the PartPs are now much longer than just one word and much longer than the direct objects in the two sample sentences. Therefore, it is only natural that the word order of construction, (where the heavy PartPs are positioned sentence-finally) is preferred since it yields a (in terms of processing) more economic word order.'' If, on the other hand, the direct object splits up the PartP (i.e. is inserted between the particle and the following directional adverbial as in (49)), yielding construction,,, then the sentences sound awkward since the expressions that belong together conceptually do not stand together contiguously, violating Behaghel's Law (cf. Behaghel 1930, 1932) or derivations thereof (although the principle of end-weight is satisfied). (49) a. So Tom took along Peter [p,, past the new Pump House]. b. Fred put down the book [,,H on the table].

Finally, construction;, with a pre-posed PP is also not a good alternative by

60

MULTIFACTOR1AL ANALYSIS IN CORPUS LINGUISTICS

means of which one can leave together structurally what belongs together conceptually since then the word order severely violates the principles of EIC and end-weight: the heavy elements are not positioned sentence-finally; cf. (50). (50) a. *So Tom took [HarlP along [,,,, past the new Pump House]]

Peter. ' ' PartP=5 NP=1 b. *Fred put [,,artP down [,,P on the table]] the book. PartP=i NP=2

By now, it should have become clear thai presence of a directional adverbial can in principle be related to both semantic and morphosyntactic determinants of processing. However, this does not pose a problem to the present analysis since, to whichever hypothesis we relate presence of a directional adverbial, the predictions deriving from that variable are identical: following directional adverbials yield a preference for construction,, be it on the grounds of length and processing of the particle and the directional adverbial or on the grounds of the semantics of the two constructions. Next, it was argued that if the particle is identical to the preposition of the following directional prepositional phrase, then construction,, is preferred. This variable does not as readily relate to the Processing Hypothesis proposed above. Still, it was shown above how the two constructions differ in their processing requirements irrespective of the (specific properties of the) direct object; more specifically, construction, was claimed to place a burden on both the speaker's and the hearer's working memory as both have to wait for the production and the comprehension of the particle respectively. Now imagine a situation where the speaker produces an utterance using construction, so that the particle is positioned sentence-finally In this case, the hearer will wait for the particle to complete the verb phrase. If, however, the particle of the TPV is uttered twice (from the speaker's perspective, as the first word of the following directional PP - from the hearer's perspective, for a split second, as a senseless repetition of an item already given) it is plausible to assume that the hearer will be slightly confused. In other words, construction, would in such circumstances be even harder to process for the hearer than it is already on its own. Therefore, it is plausible to assume that speakers will tend to avoid this constellation in order to facilitate communication. That is, this variable is also related (though more remotely) to processing aspects. Admittedly this is up to now only a speculation that needs to be supported by psycholinguistic experimentation, where reaction times and EECs could be used to measure whether subjects are surprised by the two occurrences of the particle.1" Finally, consider Arnold and Wasow's (1996) evidence on how production and planning constraints influence particle placement. Their observation is intimately related to the length of the direct object and ties in very nicely with the Processing Hypothesis. It is natural to assume that (long) direct objects that are difficult to plan and produce require more processing effort while their exact formulation has to be figured out; on the other hand, direct

KEY NOTIONS AND HYPOTHESES

61

objects that arc easy to plan and produce require less processing effort. Since Arnold and Wasow found that difficult and simple direct objects tend to occur in construction,) and construction, respectively, these predictions fit those of the Processing Hypothesis perfectly. 4.6 Interim summary

So far we have seen that many of the variables discussed in Chapter 2 have been integrated into the hypothesis put forward in this study while some have been argued to be irrelevant to the processing cost of the VPC. However, the attentive reader may have noticed that not all of the variables discussed above have been incorporated so far, namely the phonetic shape of the verb, the habitual sense of the verb and, finally, the degree of cognitive entrenchment/familiarity. This is owing to their lack of empirical support (or even their empirical falsification) and their inherent difficulties, and they will not be analysed any further. As to the variables to be included in the remainder of the analysis, we have seen that most of them bear a close relation to aspects of processing an utterance within a given stretch of discourse. Thus, prirna facie evidence for the Processing Hypothesis, which postulated a causal relationship between the choice of a construction and its processing effort, has been gathered. The processing effort of an utterance was shown to derive from several interrelated factors so this chapter can be neatly summarized in Figure 4:2. ' However, so far the relation between the variables and the Processing Hypothesis has only been established on an argumentative basis. If the

phonological aspects, e.g. • stress of the direct object

construction0

morphosyntactic aspects, e.g. • length and complexity of the direct object • early/late completion of the phrasal verb

semantic aspects, e.g. • idiomaticity of the VP • concreteness of the referent of the direct object

high

"

processing (cost) of the utterance

low information-structural aspects, e.g. • last mention of the direct object's referent (identifiablity) • times of preceding mention of the direct object's referent (degree of inactivition) construction..

Figure 4:2 Determinants of processing effort and particle placement

62

MUITIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

Processing Hypothesis was correct we would also expect to find empirical support for it that does not suffer from the drawbacks of other accounts outlined in section 2.6. Following established statistical convention, the statistical hypotheses following from the line of reasoning so far are: (51) H(l: There is no statistically significant relation or difference (be this relation or difference measured by using means, correlation coefficients or absolute/ relative frequencies) between the moiiofactorial statistical values of the variables calculated for construction() and those calculated for construction { . (52) H|: There is a statistically significant monofactorial relation (again, irrespective of the statistical technique used) such that direct objects with a high amount of processing cost (as indicated by the variables investigated in the ways argued for in the previous sections in this chapter) correlate with construction0 whereas direct objects with only little processing cost correlate with construction |.

The following chapter introduces the nature and the treatment of the data, while Chapter 6 looks at a multitude of empirical results in order to sec whether the Processing Hypothesis is supported or not. Notes 1 As the formulation of the Processing Hypothesis suggests, there are two different perspectives on these two criteria, the first depends only on S's assessment of the state of the concepts in H's mind (cf. (1)), while the second depends on the state of the concepts in the mind of S (cf. (2)) - however, we may not need to choose categorically between them. Typically, S can assume that the processes in H's mind are in harmony with those in his own mind. For the moment, we will concentrate on the processes in S's mind since it must be S's assessment of his own and H's mental processing that takes priority if the language is to perform its communicative function satisfactorily: '[1 language works best when the expression of activation cost is listener-oriented' (Chafe 1994: 75; cf. also Siewierska 1988: 84—5). In sections 7.2 and 7.3, we will return to the question of which of the two perspectives on mental processing (S's or H's) is more important for VPCs; moreover, Chapter 8 is devoted to another analysis, which is conceptually different from but compatible with the Processing Hypothesis. 2 Note that the notion of activation is used here not in the psycholinguistic sense; cf. Ch. 8 for a more formal treatment of activation within contemporary interactive activation models. 3 Note that two kinds of newness of a concept arc involved: a concept can be discourse-new and/or hearer-new (cf. Birner and Ward 1998: 14—15; Lambrecht 1994: 105-9; Prince 1981: 235 7). Following Prince, unidentifiable (i.e. hearernew and, thus by necessity, at the same time discourse-new) referents arc called brand-new, identifiable but inactive (i.e. hearer-familiar but discourse-new) concepts are called unused. 4 Cf. the obvious parallelism to the above-mentioned line of reasoning by Bolkcstein and Risselada (1987) concerning cohcsiveness. 5 If I say that active referents do not require much processing effort, then this is meant to refer only to the processing effort resulting from the givenness of

KEY NOTIONS AND HYPOTHESES

6

7 8

9

63

the referent, leaving aside other determinants of processing effort. It is possible (though probably not very likely) that a given referent is encoded with a lot of rnorphosyntactic material. In such a case, the discourse-functional processing effort of the direct object's referent would be low whereas the morphosyntactically motivated processing effort would be high. Even such a simple example supports my above reasoning, namely that interactions between different variables or variable groups need to be accounted for. In further sections of Chapter 4 where I deal with individual variable groups. I am only concerned with the respective variables' impact on processing, in the same way as 1 was just concerned with discourse-functional determinants of processing cost only. The question may arise why the focus on some linguistic expression should increase its processing cost. Apart from the intuitive appeal of this idea, it has been shown (cf. Roth 1997: 228-31) that the allocation of attention by the brain is to a large degree determined by the newness and/or the importance of the entities to which attention is allocated; naturally attention is allocated to those experiential aspects that are new and/or important so that such a concept will be processed more thoroughly, thereby increasing its processing cost. The literal meaning of VPCs is spatial due to the prepositional nature (i.e. the locative/spatial meaning) of the particles. One might object to the structural parallel given above by correctly pointing out that the two sentences exemplifying the caused-motion construction have full prepositional phrases as directional phrases rather than just a particle. However, it has in different (one could even say, opposing) linguistic schools been argued that the particle in TPVs instantiates a prepositional phrase without an overt NP or, put differently; that panicles are intransitive prepositions (cf. Transformational-Generative treatments by, e.g., Emonds 1972: 547-8 or similar discussions by Aarts 1989: 283, 1992: 81; Den Dikken 1995: 270; Radford 1988: 90—100). Analogously, it has been postulated that 'particles are not distinct from the class of prepositions: they are simply prepositions employed in grammatical constructions where the landmark happens not to be elaborated, as it otherwise normally is' (Langacker 1987: 243). Thus, there is clearly a close structural similarity between the caused-motion construction and construction, even if we find no structural identity. This similarity is even more obvious if we consider that Talmy (1985: 102 10) has shown that motion events can be decomposed such that semantic features like PATH can be separated from the verbal nucleus and Icxicalized as independent satellites in order to foreground the respective semantic feature, here PATH (observe the parallel to the historical development of VPCs); not surprisingly, the first example Talmy mentions is VPCs. One might also object to equating the directional prepositional phrases of the caused-motion construction with the particles of VPCs which sometimes have a rcsultative meaning rather than a spatial one. However, in terms of metaphorical relations along the lines of Lakoff and Johnson (1980), achieving a result is reaching the enclpoint of a path. Thus, again we have a semantic (metaphorically motivated) similarity between both construction types. This tendency can be related to the fact that syntactic processing is immediate (i.e. the processing of an clement within an utterance takes place immediately after the element has been encountered by the listener; cf, e.g., Tyler and Marslen-Wilson (1977), Carlson and Tancnhaus (1988), Clifton et al. (1991), to name but a few).

64

MULTI FACTORIAL ANALYSIS IN CORPUS LINGUISTICS

10 This strong interdependence of verb and panicle in idiomatic VPCs might be responsible, for a variety of scholars suggesting that the verb and the particle belong together so strongly that the particle forms a part of the verb; recall Bolingcr's (1971) suggestion that in construction,, the particle behaves like a verbal affix. Especially, but not exclusively, in the Transformational-Generative paradigm, a variety of different proposals have been made. Stowell (1981) proposes a rule of NP Incorporation and a rule of Particle Incorporation, where an NP and a particle respectively are incorporated into a verb. For construction,,, Particle Incorporation yields a new complex unit (a verb with an incorporated particle) that subcategorizes for an NP (for the sake of completeness, for construction,, NP Incorporation followed by Particle Incorporation yields a new complex unit, namely a verb with an incorporated direct object). For a similar approach also using particle incorporation cf. Baker (1988); other approaches to particle placement based on a complex verb analysis of the VPG are Johnson (1991, 1992); Neelernan (1994); Radford (1988). Collins and Thrainsson (1996) also rely on incorporation (of the particle into a covert be in V,) to derive construction,, from construction,. Their paper is worth quoting for what they claim to be a justification of the movement processes they postulate: 'Perhaps the particle is optionally analyzed as affixal in some sense, which we will write as Prt [affix].' (Collins and Thrainsson 1996: 431; my italics). Given this level of abstractness and optionality, this 'justification' is singularly unhelpful, and Collins and Thrainsson seem to be unaware of Bolingcr's somewhat less vague proposal. Furthermore, Lindner's (1981: 189) analysis also supports the assumption of 'alternative constituencies': construction,, and construction, arc argued to differ in terms of constituency such that in construction, V + NP constitutes a composite scene to be modified by the following particle whereas in construction,, V + Part constitutes a composite scene; cf. above n. 22 for some comments on the constituency of VPCs and Langacker (1 997) for a more detailed account of constituency in Cognitive Grammar. 11 This claim is supported by the independent observation that idiomatic expressions are in general much less susceptible to syntactic rearrangements than literal expressions. 12 Consider, for instance, the verb heal. Beat can be used transitively (as in, say, He had a gilt-edged opportunity to beat Stephen Hendry, so far the man of the season with four first prizes) or it can be used with a particle (namely up) as a TPV (as in, e.g.. The last time I cut my hair my father beat me up very badly). Thus, in the case of TPVs, it frequently is the particle which allows disambiguation. To my knowledge, there arc no experimental data on this in English, but experiments on the basis of event-related potentials (ERP) for German separable complex verbs have revealed that, for sentences such as Er lachelte den Bauherrn an, one can show that the particle an (which changes the intransitive verb Idcheln into the transitive verb anlacheln) is accompanied by the so-called P300 component, which is generally taken to reflect the resolution of an uncertainty (cf. Urban and Friedenci 1999a, 1999b). Thus, the evidence from German at least supports the line of reasoning for the English data although investigation of English data is still necessary. The question arises as to why there are two constructions at all if one of them is inherently simpler to process than the other one. This has already been quite problematic for Hawkins (1994) who has argued that construction, is basic rather than construction,,. The reason for this is that if construction,, was basic, then there would be no reason why construction, should have come into

KEY NOTIONS AND HYPOTHESES

13

14

15 16

65

existence at all since construction, is optimal as far as the processing of the immediate constituents is concerned. Cf. section 7.1 and Chapter 8 below for further discussion of this issue. These two terms are naturally related to the slighdy more elaborated differentiation of concepts in Figure 4:1. A variety of other dichotornous terms has been introduced as being related to or compatible with the given-new distinction such as topic-focus; topic-comment; thcmc-rheme. However, these terms are not uniformly used in functional schools of linguistics and can, thus, not be used interchangeably. I use given and new here since (i) these simple terms suffice to communicate what I intend to say without being as theory-laden as the other proposals and (ii) the remaining discussion will employ the terminology used in Figure 4:1 anyway. For an attempt to bring order into the terminological 'chaos', cf. Ostman and Virtancn (1997); for an approach defining topic and focus in relational rather than absolute terms, cf. Lambrecht (1994). Recall that the account of constituency used here allows for semantic, phonological and classical constituents; cf. n. 12. My use of the term PartP is simply a convenient label for this semantic constituent without implying that PartP is a classical constituent such as PP, which could be identified using classical movement tests. In this respect, the motivation for arguing that the particle and the PP make up a semantic constituent is straightforward: the particle and the PP belong together scmantically in that together they provide all the spatial information about the movement process the referent of the direct object undergoes. However, there is also a second line of reasoning supporting the preliminary analysis of the particle and the following PP as a so-called PartP. This argumentation is concerned with the way the hearer comprehends (or, more precisely, parses) the incoming string. There are some well-known parsing principles such as Right Association (Kimball 1973), Late Closure (Frazier 1979, 1985) or Hawkins's EIC. and what these principles have in common is that they predict a preference for low attachment of incoming phrases to the already existing phrase marker. According to these principles, attaching the PP to the particle (yielding what I have called PartP) would be the natural way to parse sentences with construction, followed by a directional PP. As early as 1919. Van Dongen observed that long particles such as toge/her also prefer end-position, which ties in nicely with our observation concerning 'particle phrases'. There is some prima facie evidence for my assumption that hearers might be surprised to encounter the same particle twice: on the basis of all oral data in the BNC about 10 million words), we can determine how likely it is that (i) any two adverbial particles (BNC-tag: PRP) follow one another (e.g. down in), and (ii) the same adverbial particle occurs twice by calculating a measure of collocational strength, namely Mutual Information (MI; cf. Church and Hanks 1990, Oakes 1998); this statistic takes on positive values when two items (e.g. words or tags) co-occur more often than expected and negative values when items occur in complementary distribution. Consider the table below for the results. As is obvious, in the vast majority of cases where one adverbial particle follows another one, MI is negative irrespective of whether the particles are identical or not. This is not conclusive evidence for my claim, but it still supports the above assumption that hearers will rarely encounter two adjacent particles, let alone the same particle, twice - the rare cases where this happens are mostly

66

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS Collocational strengths of two subsequent adverbial particles in the spoken part of the BNC

MIX) MKO Column totals

PRP, = PRP2

PRP, * PRP,

Row totals

2

10 331 341

12 372 384

41 43

disfluencies and (floor-holding) repetitions. In a cognitive framework, this structural pattern will thus be barely entrenched and may indeed result in surprise. 17 Figure 4:2 is not to be seen as a causal model where morphosyntactic, semantic and information-structural variables are independent from (since not graphically related to) one another (cf., e.g., the obvious inverse correlation between length and givenness discussed in section 4.3) -- it is just an informal sketch of which factors are related to processing effort. For a more advanced discussion of these matters, cf. Chapter 8.

5

The data

This chapter will be concerned with the data investigated in the present analysis. Section 5.1 explains how the data were gathered in order to obtain a representative sample. Then, by means of detailed exemplification, section 5.2 discusses how the values/levels of the variables to be investigated were assigned to each of the example sentences. 5.1 Origin of the corpus data As already repeatedly stated, the present work advocates an empirical approach where psychologically real factors (or, more precisely, their linguistic correlates) are used for the analysis of naturally occurring data. Consequently, the perspective taken here is rigorously corpus-based in order to find out what is actually done by native speakers. This study relies on 403 instances of the VPC taken from the British National Corpus and I will start by explaining how these 403 sentences were selected. While I have argued against introspective analyses of introspective data several times, the use of corpora in linguistics is by no means a guarantee for attaining objective, reliable and valid analyses: corpora can be biased since, at least for so-called general corpora (as opposed to specialized corpora; cf. Kennedy 1998: 20), it is difficult to reach an agreement on how the key goal of representativity can be safely achieved. What is more, even if a reasonable degree of representativity of a corpus has been reached, it is still possible that the sample drawn out of such a corpus is skewed by not containing the examples typical of what is being examined. Several precautions were taken in this study to avoid such deficiencies. In order to find the most frequently used instances of TPVs, I checked the Cambridge International Dictionary of Phrasal Verbs (1997) for all verbs that were listed as principally allowing both construction,, and construction ( . To those I added TPVs discussed in the literature and entries from other dictionaries and works such as the BB1Dictionary of English Word Combinations (1997) and the Oxford Dictionary of Phrasal Verbs (1993) (cf. the end of Chapter 11 for a list of verb sources). The result was a list of 1,357 dilferent English TPVs; this list (the largest of TPVs so far) is given in Appendix 10.3. To make sure that one can generalize from the sample to less typical/frequent instances of

68

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

TPVs, I did not base my analysis on an only marginally typical sample. Thus, I mainly concentrated on the ten most frequent particles (namely up, out, off, down, in, away, back, over, on and around, in descending order of frequency) and the ten most frequent verbs (namely put, bring, take, turn, throw, pull, call, get, keep, and kick, again, in descending order of frequency) from this list.' I used a concordancing software' to find all co-occurrences of one form of the ten verbs and one of the particles where the particle was located up to sixteen words to the right of the verb form.3 This was done once on corpus hies containing only written text and once on corpus files containing only spoken language, yielding two separate concordance files. Prior to the closer inspection of both output files, the concordance lines were sorted according to the file names where the data originated from; this is the most economic way of randomizing the data so that no text type is preferred since, according to the manual of the British National Corpus, 'the three-character identifiers [i.e. the file names] used (and hence the directory structure) are entirely arbitrary and do not convey any information about the type of text contained' (Burnard 1995: 139). The listed concordance was then manually scanned for instances of VPCs since lilerally hundreds of examples had to be weeded out, namely cases of passives of TPVs (cf. (53)), instances of phrasal-prepositional verbs (cf. Quirk et al. 1985: 1160-1; cf. (54)) and cases where the verb and the particle did not occur in the same sentence (cf. (55)). (53) . . . when the Nissan Serena was brought out. (54) We need to put up with these problems. (55) So that's why I didn't take it. Well it wasn't working in the field that I want to work in.4

In this way, 200 VPCs in spoken language and 203 VPCs in written language were extracted for further investigation and stored in separate files as a sample of 403 cases. This sample is controlled for with respect to the (for all practical purposes equivalent) numbers of cases per register and mainly consists of the most frequently used TPVs in order to exclude too many marginal and barely representative instances of VPCs.5 Table 5:1 shows the distribution of the 403 clauses making up the sample depending on the register. Table 5:1 Distribution of the 403 sample sentences

Construction,, Construction, Column totals

Spoken

Written

Row totals

67 133 200

127 76 203

194 209 403

THE DATA

69

Since many of the variables are not just concerned with the particular sentence in which particle placement occurs, the next step was to establish the requisite context for each sample sentence. For each of the cases, the preceding and subsequent ten clauses were added so that, taken together, the analysis is based on several thousand individual clauses.'1 In establishing the context, expressions were counted as clauses only if: • they contained a noun phrase or a clause as grammatical subject as well as a finite verb; or • they were participial or gerundival clauses (such as. e.g., the non-italicized part in The new rules forbid more than one to put up a sign, a rule usually ignored); or • a new conversational turn started. However, in order not to exclude too much data from consideration, the following cases were not counted as full clauses on their own even if they met any of the above-mentioned criteria: question tags; discourse markers such as you know, as it were, I mean] cleft sentences and false starts. Then, for each of the cases, numbers were assigned representing the values/levels of the variables for the respective case, which will be explained in some detail in the following section. 5.2 Treatment of the corpus data The basis for the statistical analyses lo be discussed in Chapter 6 was a huge table containing each sentence and the figures that describe its characteristics with respect to the independent variables mentioned above. In this section, I will illustrate (mainly in tabular form) how the utterance characteristics were encoded, which also involves (i) comments on the measurement scales of the variables involved and (ii) examples from the corpus data in order to clarify the coding process. First, each sentence was assigned a value representing the register of the sample sentence, i.e.. the nominal variable register (REGISTER) has two levels and. thus, can take two values, namely one for spoken data (0) and one for written data (1). Second, each sentence was assigned a value representing the levels of the choice of construction, i.e., the dependent nominal variable in this study, construction, can also take on two values, namely 0 for construction,, and 1 for construction, (cf. Chapter 1, n. 5). The variable JVP type of the direct object (TYPE) is, in this study, a nominal variable. It comprises the levels pronominal, semi-pronominal, lexical and proper name. Table 5:2 provides some guideline of how the sentences from the corpus data were encoded. Determiner of the direct object (DET) is a nominal variable, too. The set of possible levels were no determiner, indefinite determiner and defmile determiner; cf. Table 5:3. The variable complexity of the direct object (COMPLEX) is measured on an ordinal scale; three different values of increasing complexity are

MUI.T1FACTORIAL ANALYSIS IX CORPUS LINGUISTICS

70

Table 5:2 Encoding of NP Type of the Direct Object (TYPE) Level

Possible sub-categories

Examples from ihe corpus data

pronominal

personal pronoun possessive pronoun demonstrative pronoun reflexive pronoun indefinite pronouns refereritially vague nouns

Put it back later (no examples such as, e.g., mine found) Make sure you take these down please It won't be able to warm itself up I'd have to put something else in we put this matter down till later in the day it brought back memories I put my head down low enough we can actually take out the files The lawyers took Valenzuela off to record his testimony . . . that bring France down

semipronominal lexical

proper name

Table 5:3 Encoding of Determiner of the Direct Object (DET) I^evel no determiner indefinite determiner

definite determiner

Possible sub-categories

Exampksfrom the corpus data

it brought back memories plural forms personal pronouns In that case put it back to later in the day typical indef. determiner let's put a word in for you general determiner the lawyers have patched up some sort of agreement if you ever put any lettering on typical def. determiner I could have put the headings on possessive pronouns Could you just take your coats off? demonstrative pronouns in the future they will carry on that practice

distinguished, namely simple direct object NPs, intermediate direct object .NPs and complex direct object.NPs (see Table 5:4). The final morphosyntactic variable is length of the direct object (measured in words, LENGTHW, and in syllables, LENGTHS).' For this interval variable, the number of words or syllables of each direct object was simply counted and entered into the table. For instance, the direct object in (56a) is three words/ four syllables long, in (56b), the direct object consists of five words/ten syllables and in (56c) it is one word/three syllables long. (56) a. They will take off [NP their own clothing]. b. You know a lot of have sort of brought up [ XI , the alternative sort of er medicine].

71

THE DMA

Table 5:4 Encoding of Complexity of the Direct Object (COMPLEX) Ijtmll Value simple (0)

Possible sub-categories

pronominal NPs NPs consisting of (Dot +) N intermediate NPs with adjectival (T) modifiers NPs with a genitive co-ordinated NPs

complex (2)

Examples from the corpus data if you're going to put them out Put your hand up

they brought in private buses to their local areas we have to put descriptions of levels down that hopefully would lake away any dirt and extra bits NPs with embedded finite they could bring back trams which are or non-finite (e.g. much less in terms of pollution participial or He also took over some national gerundival) clauses (cf., functions handled now by the Metropolitan Police e.g., Ross 1986: 33)

c. But then it was the right decision for the longer term, to bring down [N-P inflation].

Note that hesitations such as er in (56b) or um were not counted as contributing to the length of the direct object, although they were included as production difficulty (cf. below). The first semantic variable is animacy of the referent, of the direct object (AiviMACY). It is nominal and two levels are distinguished: animate (i.e. 1, for human beings as in (57a) and animals as in (57b) and (57c)) and inanimate (i.e. 0, as in (57d)).B (57} a. b. c. d.

1 am going to bring down a lot of other people with me. I put up a startled grouse that exploded into flight. We'll put him [a bird] back into the marsh around. I was coughing up blood from the stomach.

A second nominal semantic variable is concreteness of the referent of the direct object (CONCRETE). Again two levels were possible, namely abstract (i.e. 0, cf. (58)) and concrete (i.e. 1, cf. (59)), depending on whether the referent is visible and physically manipulable or not. (58) a. b. (59) a. b.

In the future they will carry on that practice. I just wanted to bring up the point about secrecy. Can you get your fingers out? He tore it [an illegal barbed wire fence] down.

Closely related to the abstractness/concrctcncss of the direct object's

72

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

referent is idiomatic meaning of the verb phrase (IDIOMATICITY). In this study, I followed the approaches of Bolinger (1971), Cowie and Mackin (1993: ix) and Fairclough (1965) and took this variable to be measured on an ordinal level, differentiating between literal, metaphorical and idiomatic sentences. A sentence was counted as literal (i.e. it was assigned the value 0) if the meaning of the whole expression was totally predictable from the meaning of the parts (which was generally equivalent to the referent of the direct object undergoing a change of location in a manner specified by the verb); cf. (60). (60) a. You might at least put away your socks, b. You can stick the pin in.

A sentence was classified as being metaphorical (getting the value 1) if its meaning was not fully predictable from the meaning of its parts because of, say, violations of selectional restrictions that could be accounted for with reference to simple metaphorical or mctonymic mappings9 as introduced by Lakoff and Johnson (1980) or, more importantly, preference violations (cf. Fass 1991: 59-68 on the met* method); cf. (61). ' (61) a. I put down comments. b. When writers go abroad they bring back ideas. c. But then it was the right decision for the longer term, to bring down inflation.'"

Lastly a sentence was categorized as idiomatic (getting the value 2) if the meaning of the sentence was not predictable on the basis of the parts alone and maximally two simple mappings (cf. (62)). In cases where this classification procedure seemed only slightly problematic, the degree of idiomaticity was checked using the Longman Dictionary of Phrasal Verbs (1983), where idiomatic expressions are accordingly marked. (62) a. Cerda interviewed those [...], and then threw down the gauntlet to Pinochet, b. Divers should lake out decompression insurance.

The last semantic variable to be dealt with is semantic modification of the particle. However, while its encoding would have been quite easy (either there is an aspectual or perfective marker or not), not a single instance was found in the corpus data, so this phenomenon seems to be very rare, which might be the explanation for the fact that this variable was only considered by a small number of authors.'' Let us now turn to the variety of discourse-functional variables. The clearest and yet most economic way of illustrating their encoding is by means of an example where the whole context of an utterance with a VPC is investigated simultaneously. Table 5:5 is an instance from the written part of the British National Corpus. The first column displays the position of

THE DATA

73

Table 5:5 Example sentence with context from the British National Corpus (Fik-: A91) -10 IT WAS a full year t -9 before he made the break -8 but eventually, on August 21, 1984, he went over to one of the more independent local magazines, Cauce, and asked for its leading journalist Monica Gonzalez. —7 The resulting interview was heavy going for both of them. -6 Monica had been a close friend of Ricardo and Jose Wcibcl. —5 Now she was listening to their kidnapper. —4 What she remembers most —3 was his self-disgust and terror. —2 Monica rang lawyers at the Vicariate of Solidarity; — 1 which had been set up by the Catholic Church in 1975 and was by now Chile's leading human rights organization. 0 The lawyers took Valenzuela off to record his testimony. 1 A few days later, Valenzuela went underground. 2 After several months in hiding he crossed into Argentina and left for France. 3 Monica's interview was published in December 1984 4 and its impact was swift and savage. 5 First to suffer were the relatives of those 6 who had been kidnapped by the Joint Command. 7 They now knew the terrible truth, 8 even though without a body they still could not mourn. 9 They gave Valenzuela little credit for speaking out. 10 'The impact on the Vicariate of Solidarity was even more violent.

clause in the right column relative to the VPC (which itself is accordingly at position 0). The direct object of the VPC under consideration is Valenzuela. Its coreferential expressions arc printed in bold type for expository reasons. The variable news value of the referent of the direct, object is not explicitly encoded for the simple reason that it is operationalized by means of several other inherently much more objective variables to be discussed shortly. The variables last mention of the referent of the direct object (LM) and next mention of the referent of the direct object (Nivi) are nominal: either the referent of the direct object has been mentioned before or after ihe VPC (value: 1) or not (value 0). Looking at the table, first, it becomes obvious that Valenzuela is referred to several times both before and after the VPC. so for this instance each of these variables is assigned the value 1. Second, the last mention of Valenzuela is two clauses before the VPC (namely, in clause - 3) so the variable distance to last mention of the referent oj the direct object (DTLM) gets the value 2; analogously, Valenzuela is mentioned in the same sentence again, which is why distance to next mention of the referent oj the direct object (!)TNM) gets the value 0 (representing no clausal

74

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

distance between the different occurrences).12 Thirdly, in the preceding context, Valenzuela is referred to four times (he, he, their kidnapper, his, we will discuss them later) so that times of preceding mention of the referent of the direct object (TOPM) takes on the value 4, in the subsequent discourse, Valenzuela is referred to four times (his, Valenzuela, he, Valenzuela; we will discuss Joint Command further below) so that times of subsequent mention of the referent of the direct object (TosM) takes on the value 4. From this, it follows that the value for overall mention (Oivi, as a potential measure of the overall importance of the referent; cf. section 2.6.1) is 9 (4 from the preceding discourse. 1 in the VPC itself and 4 from the subsequent discourse). The final two discourse-functional variables that remain measure the cohesiveness of the direct object's referent to the preceding and the subsequent discourse respectively (CcmPC and CoaSC). It was argued previously that simply counting co-referential expressions does not suffice as a means to determine the contextually dependent degree of activation of a referent - however, it was also argued that the notion of cohesiveness as defined by Bolkcstcin and Risselada (1987) is too powerful to be empirically useful (cf. Chapter 2, n. 20). Therefore, in this study I have restricted my attention to three different ways in which the cohesiveness of an entity to the context (i.e. its degree of activation due to contextual clues) can be increased. First, two different ways of increasing the cohesiveness of the referent X of a linguistic expression without explicitly naming a co-referential expression are considered here: 1 If a linguistic expression in the context (be it preceding or subsequent) names a superordinatc term or a subordinate term of X, then the degree of cohesiveness of X to the respective context is increased by one. For example, in Have a look a those tulips. Anyway, I like flowers in general, flowers is cohesive to tulips as it continues the activation of the referent of those tulips in the preceding sentence. 2 If an expression in the (preceding or subsequent) context names a part of X or the whole to which X belongs, the degree of cohesiveness of X to the respective context is increased by one (e.g. in sentence 6 in Table 5:5, the referent of the NP the Joint Command, of which Valenzuela was a part, continues the activation of the concept Valenzuela). Second, of course, co-referential items can also increase the degree of activation of the referent of a linguistic expression X; since using strictly coreferential expressions is the most effective way of continuing the activation of a referent, strictly co-referential items increase the degree of cohesiveness by two. If we accordingly sum up the values for the cohesiveness to the preceding discourse and the cohesiveness to the following discourse, we arrive at the values 9 (4 X 2 for 4 co-referential expressions and 1 for them] and 9 (4 x 2 for 4 co-referential expressions and 1 X 1 for the Joint Command] respectively. Finally, three variables remain. The nominal variable presence of a

THE DATA

75

directional adverbial (PP) comprises two levels and can take on either of two values, depending on whether there is a directional adverbial following the direct object or the particle (i.e. the value 1, cf. (63)) or not (i.e. the value 0, cf.(64))/ (63) a. I would urge the panel to send out [XF their proposed leaflet] [pp to the ministers in various areas]. b. Why don't they put all [_Np the leaders of all the countries] up [pp in the air]? c. It has taken many years to bring [XF the town] up [rf to the standard]. (64) a. Fitt brought down the Labour Government, b. The person cannot draw air in.

The next variable is nominal, too, and it is concerned with whether the particle is identical to the preposition of the following directional adverbial (PART = PREP). It is hardly in need of detailed exemplification: (65) is an instance of a sentence (of only two in the whole of the corpus data) which was classified accordingly. (65) It means you can pack in [xr a lot more things] [pp iri your day].

Finally, let us turn to the interval variable production and planning effects (UISFLUENGY) of Arnold and Wasow (1996). For each clause with a VPC, I preciselyfollowed their approach and counted the number of hesitations such as um and er, false starts and repeats and repairs. Consider (66). (66) a. if you take out er a large cross section b. it can, can er take the stuff [coal] in from the er from the ports

In (66a), there is just one disfluency (namely er) so this sentence was assigned the value 1: in (66b), there is one repetition (can, can) and two signs of hesitation (er), so this clause is assigned the value 3. It needs to be noticed, however, that disfluency was of course only found in the oral data and diat the number of clauses with any disfluencies at all was quite low, namely 15, so statistically significant generalizations should not be expected. After this fairly detailed discussion and exemplification of the coding of the variables for the analyses,'1 Chapter 6 will briefly comment on the statistical techniques to be used and illustrate the multitude of findings from the statistical investigation of the corpus data. Notes 1 In the literature, sometimes slightly differing lists of particle frequencies are given; cf, for instance, Kennedy (1920); Frasor (1965); Nelson et al. (1982); O'Dowd (1994). Iri view of the large overlap of these lists it would be pointless to discuss these different figures at length. I will, therefore, only provide a summary of these proposals:

76

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS • the verbs mosi often found in these combinations are back, blow, break, bring, call, come, fall, get, give, go, hold, keep, lay, let, look, make, put, run, send, set, stand, lake, turn, work, • the most prolific particles are about, across, around (round), aside, away, at, back, by, down, for, forth, in, off. on, out, over, through, to, up, with.

2 3

4

5

6

The (to my knowledge at least) most up-to-date account providing frequency data is O'Dowd (1998). However, in her list (1998: 32) and some comparable ones from above, the frequency information is based on the frequencies of the particles in every grammatical form so that:, for instance, prepositions of intransitive prepositional verbs were also counted. Since these constructions and their contribution to particle frequencies are not relevant to my study focusing only on TPVs, I decided to use only the particle frequencies of actual VPCs as defined above (cf. section 1.1). For our practical purposes the differences are onlymarginal: apart from two particles my list is identical to O'Dowd's even if the percentages differ markedly. The software used was WordSmith Tools 3.0. There is no special motivation underlying the choice of sixteen as the right-hand margin. However, the studies by Chen (1986), Gries (1999) and Hawkins (1991, 1994) have unanimously shown that the probability of construction! with a direct object longer than just four words or six to nine syllables approaches zero, so sixteen words is sufficient to assume that the searching procedure does not unintentionally eliminate surprisingly long direct objects from consideration. The sample also contains some instances of VPCs that do not consist of the ten most frequent verbs and particles listed above. In order to arrive at a not too homogeneous sample, T also included all other VPCs I came across by accident. For example, the concordaricer provided one instance where the verb get and the particle on were found together without constituting a TP\J but the following sentence contained the TPV to loosen off. This was also included in the sample. The question may arise why 'only' 403 exam [lies were used for the analysis. First, although this may seem quite limited at a first superficial glance, it has to be observed that, with 403 cases, this is by far the largest quantitative analysis of particle placement ever undertaken (cf. Hawkins' (1994) analysis of a mere 179 cases or Chen's (1986) analysis of only 239 cases). In other words, the given study investigates nearly as many cases as the two most recent largest analyses together. Additionally, while it is frequently possible to calculate necessary sample sizes on the basis of an expected effect size i'cf., e.g., Cohen 1983) this is not possible for the present design, which is to a large degree exploratory in nature. Thus, while one could of course always say 'more data would be better', it is quite impossible to a priori recommend a minimum number of items on a principled basis. Be that as it may, the results to follow show that the predicted effects are all quite strong and highly significant so that the number of cases is not too small at all. What is more, section 6.3.2 discusses some additional results that will corroborate my claim that the sample size is not too small beyond any doubt. Again, there is no principled basis on which the choice of ten clauses as discourse context is based. Other studies have considered different numbers of clauses but have also admitted that the decision on a specific number is an arbitrary one (cf, e.g., Givoii 1992: 49 n. 17). Moreover, even if some other study has included more than ten clauses from the context, the definition of clauses used differ at times. Note also that in some cases ten preceding or

THK DA'IA

77

subsequent clauses could not be included, e.g. when the clause with the TPV was the first or last of a text. 7 One might wonder whether this distinction is in fact warranted. First, I mentioned in section 2.2 that different authors used different measures of length, which is why the present analysis can test which measure is better suited to the purpose at hand. Second, I know of at least one case (Kintsch 1972, reported in Bock 1982) where the correlation between a dependent variable and LENGTH\\'' was not significant whereas the correlation between the same dependent variables and LENGTHS yielded a significant correlation. 8 There were no instances of the intermediate class of plants as direct objects in the data. 9 A simple metaphorical mapping is loosely defined as a mapping making direct reference to a cogiiitivcly simple and real experience. For instance, in this sense SELF-INITIATED CHANGE OF STATE (ACTION) IS SF.I.F-PROPELLED MOTION is quite a

complex metaphor whereas CHANGE is MOTION is the simpler and more fundamental one underlying the first (examples are taken from the Master Metaphor List at the Conceptual Metaphor Homepage). 10 In this instance, two simple metaphors are combined for the understanding of this sentence: INFLATION is AN ENTITY (cf. Lakoff and Johnson 1980: 26) and LESS is DOWN (cf. Lakoff and Johnson 1980: 15—16). For a study focusing on the role metaphor and metonymy play for the semantics of VPCs, and for details of how different metaphors can jointly license a non-literal (i.e. non-spatial) meaning of the construction, cf. Morgan (1997). 11 The variable semantic focus of the verb phrase is not encoded. On the one hand, we have seen previously that this variable is more pragmatic than semantic in nature. On the other hand, it is difficult to see how corpus data would enable one to falsify the claim that this variable is important: there is no possibility to infer from the corpus data which referent the speaker intended to focus on. Possibly this variable can most fruitfully be investigated experimentally or with a corpus very precisely annotated with different stress levels. 12 The variables distance to last mention of the referent of the direct object and distance lo next mention of the direct object were receded for the following reason: in many instances, there is no mention of the referent of the direct object in the prior or subsequent context so that a variable measuring distance could not be assigned any value at all. This is not a problem as such, but it can be a problem in many statistical analyses since, by default, these eases enter into the analyses as so-called missing data (MD). If several variables have missing data in different eases, then one might investigate hundreds of cases, but could be forced to consider only a few, namely those where not a single variable lacks data. Therefore, DTLM (with its possible values from 0 to 9 and MD) was rccoded with values from 10 (for a distance of 0) to 0 (for no prior mention at all), as a variable called activation from the preceding context (AcrPC); for the above example, where DTLM = 2. that means that the AcrPC value is 8. Likewise, DTNM (with its possible values from 0 to 9 and MD) was receded the same way as a variable measuring the extent to which the direct object forms a cluster with the next mention (CLUsSC). For this example, where DTNM = 0, GursSC is 10. Thus, the statistical techniques are applied using these two new variables, which has no bearing on the conceptual basis of the analyses and the interpretation of the results.

78

MUITIFACTORIAL ANALYSIS IX CORPUS LINGUISTICS

13 The variables habitual meaning of the verb phrase arid phonetic shape of the verb have not been included in this discussion since they have, for the reasons given in section 2.6.1, not been taken to contribute to particle placement. The variable stress pattern of the verb phrase has also not been included here since no phonologically annotated corpus data were available. Nevertheless, on the basis of the results to be discussed further below, I will return to this variable again.

6

Results and discussion

The present study utilizes several statistical procedures ranging from verysimple to quite advanced to achieve both its linguistic and methodological goals. In order to render the results of this study more accessible to statistical laymen, I will in each section briefly comment on the procedures that were used, although it is not desirable to cover all the techniques in detail. I will, thus, explain the most essential characteristics of the techniques, all the results and their contributions to linguistic as well as further-ranging methodological issues. 6.1 Monofactorial results 6.1.1

Introduction

This section will illustrate in detail the results of rnonofactorial techniques, i.e. the extent l.o which each variable contributes to particle placement in isolation. One might ask why such monofactorial results are still dealt with, although it was argued previously that they suffer from several drawbacks. The reason for the detailed illustration of monofactorial results to follow is threefold. First, J want to test empirically whether the previous (mostly nonempirical) analyses of particle placement are supported. Second, I want to provide a descriptively more adequate characterization of particle placement, which overcomes the numerous deficiencies of a very general nature mentioned previously. Finally, the monofactorial analysis yields indices representing the absolute strength of the relation of each variable to particle placement in isolation. The most basic statistical method that will be used in this section is the calculation of frequencies to be represented in contingency tables for Chisquare analyses. For each table, the degree of significance (or the absence of it) of the observed distribution will be determined to identify typical characteristics of each construction.' In order to summarize these results for each variable in a single statistic, one can calculate correlation coefficients indicating: • the direction of the relationship between an independent variable and particle placement: a positive value represents a positive relationship,

80

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

i.e. high values of the variable eorrelate with a high value for the construction (namely construction,) and vice versa; a negative value represents a negative relationship, i.e. low values of the variable correlate with a high value for the construction (namely construction,) and vice versa;2 • the strength of the relationship between an independent variable and particle placement: the higher the absolute value of the correlation coefficient, the stronger the relationship (cf. n. 5 for nominal variables).' Apart from only observing the correlations between variables as such, we will in some cases also compute partial correlations in order to identify spurious correlations between variables. The statistical investigation of such a huge mass of data yields an enormous amount of individual results, and it would be quite tiring to review all these results at the same level of specificity. Therefore, the discussion of the results is organized as follows. Each kind of statistical result will at first be discussed quite extensively on the basis of one or two examples in order to introduce the less statistically informed majority of readers to the principles of the type of analysis and its subsequent interpretation. At a later stage, the discussion of the results will be much less lengthy. Sections 6.1.2 to 6.1.4 will deal with the results concerning the (morphosyntactic, semantic and discourse-functional) variables of the Processing Hypothesis. Then, section 6.1.5 will discuss the results concerning the remaining variables and those that I claim to be irrelevant; finally, section 6.1.6 will summarize the monofactorial results. 6.1.2 Morphosyntactic variables of the Processing Hypothesis The first variable to be dealt with here is complexity of the direct object. In the data, the distribution of the two constructions represented in Table 6:1 was obtained. In order to find out whether this observed distribution is significant (so that COMPLEX can be argued to influence the choice of construction) or not (so that COMPLEX probably does not influence the choice of construction), one first needs to calculate the expected dislribution of the two constructions, i.e. the distribution that would result from COMPLEX having no Table 6:1 Observed distribution of constructions relative to COMPLEX

Construction,, Construction! Column totals

a- >.!„ , KareNPs(O)

Intermediate .NT (I)

, ,-,_,,,,., Complex.NP (2)

Row totals

76 186 262

102 22 124

16 1 17

194 209 403

RESULTS AND DISCUSSION

81

influence on the choice of construction. In these cases (i.e. those where there are no previous distributional assumptions), the frequencies expected according to H, arc based on the so-called marginal frequencies, i.e. the row totals and the column totals, yielding the distribution in Table 6:2. From these two tables, the Chi-square value for the complete table can be calculated. In this particular case, Chi-square is highly significant, showing that the observed frequencies deviate from the expected frequencies in such a way that it is extremely unlikely to get such a distribution on the basis of pure chance (as predicted by H,,): % '(2) = 110.63; p Complex Complex .A'P (2) 194-17

403 209-17 403 17

=8 =9

Row totals 194 209 403

Table 6:3 Contributions to Chi-square for the distribution in Table 6.1 Simple < Bare .NP (0) Construction,, Construction, Column totals

19.92 18.49 38.41

Intermediate MP (J)

> Complex Complex jVP (2)

Ron; totals

29.99 27.83 57.82

7.47 6.93 11.4

57.37 53.25 110.63

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MULTIFACTORIAL ANALYSIS IX CORPUS LINGUISTICS

statisticians recommend the use of cither Dayton's correction formula, the equally conservative Bonferroni correction, Holm's correction or the technique of configural frequency analysis (cf., e.g., Bortz et al. 1990: 51-2, 155 8). In the following tables, we will, therefore, only be concerned with contributions to Chi-square that were corrected according to Bonferroni for a posteriori significance. In this case, where we have six cells in the table, the critical value of a posteriori tests of contributions to Chi-square is 6.96. That is to say, nearly all the values contribute significantly to the overall highly significant result. But what do the results mean? The answer is straightforward, given the relation between the observed values and the expected values in Tables 6:1 and Table 6:2 respectively. In each of the cells significantly contributing to the overall Chi-square (in this case, all the cells), we simply compare the observed with the expected frequency: • for bare NPs, we see that construction,, is much less frequent than one would have expected by pure chance, whereas construction, is much more frequent than expected by pure chance; this can be summarized by a correlation coefficient for bare NPs and the choice of construction: X = .49;p6 >0 >1

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MULTIFACTOR1AL ANALYSIS IN CORPUS LINGUISTICS

classes into which each dichotomized variable has been divided on the basis of its above-mentioned cut-off point. Let us now first consider a simple case, namely the variables mentioned for (69), here repeated as (71), namely COMPLEX: simple and DET: indefinite. (71) Fred turned down an offer.

In order to find out which of the two levels has a stronger influence on particle placement, we need to find the preference for a construction in the combination simple direct object vs. indefinite determiner of the direct object. But what would be a suitable way of analysis. One possibility is to simply list each variable together with its number of significant wins and losses (and draws) against other variables. Consider Table 6:27. There are 20 sentences with simple direct objects having an indefinite determiner in the corpus data. This small number is due to the fact that indefinite determiners are normally used for referents to be introduced into the discourse, which in turn arc often heavily modified and thus quite complex - as a comparison, observe that there are more than twice as many cases of complex direct objects having indefinite determiners. The question now is whether the observed distribution (11 vs. 9) deviates significantly from the expected distribution (10 vs. 10). In this case, it is quite obvious that the distribution is not significant at all (pblllomiai = .824) so that neither of the two levels (simple or indefinite) is stronger in enforcing its usual constructional preference. A similar strategy could be applied to the case of COMPLEX vs. LM. Two pairs of conflicting combinations are possible: simple (requiring construction,) vs. discourse-new (requiring construction^ and complex (requiring construction^ vs. discourse-familiar (requiring construction,). Thus, consider Table 6:28. In this case, we have to test whether the observed distributions (27 vs. 7 and 50 vs. 50) differ significantly from the expected distributions. The first of these pairs is indeed significant (p construction.

discourse-functional (preceding context) low variable values => construction,. st*m construction,.

morphosvntactic

low variable values => construction.

RESULTS AM) DISCUSSION

115

Table 6:35 Classification/prediction accuracy of three analyses Classification method A posteriori classification learning sample 200 oral sentences and 150 written sentences 150 oral sentences and 200 written sentences 174 oral sentences and 176 written sentences

A priori correct predictions 85.9%

Text sample

53 written sentences

81.1%***

53 oral sentences

67.9% **

26 oral sentences and 27 written sentences

Cross-validated a priori prediction accuracy

88.7% *** 83.9% ***

sentences is not too small (cf. Chapter 5, n. 66): the results of the discriminant analyses are highly significant (cf. Tables 6:30 and 6:33) and the crossvalidations on the basis of three smaller samples show that even smaller learning samples of 350 sentences yield comparable prediction results (cf. Tables 6:32 and 6:35). From this we can conclude that the Processing Hypothesis is strongly supported as it allows us to predict the subconscious decisions of speakers for a construction with more than 80 per cent accuracy. One methodological caveat remains, however. There are scholars who might argue that one must not apply a discriminant analysis to my data since discriminant analyses require (i) a multivariate normal distribution of the variable values, and (ii) homogeneity of variances of the variables under consideration. Moreover, some statistician might also add that discriminant analyses where categorical variables have been receded using nominal 0,1dummies can produce skewed results because of the resulting intercorrelations. As a consequence, these scholars might argue that the results of the discriminant analysis are not reliable and parameter-free/distribution-free techniques such as CART (Classification and Regression Trees) should have been chosen instead. In what follows I will briefly address these concerns. First, while many researchers tend to emphasize the importance of distributional assumptions (such as normality, homogeneity of variances and the like), there is also a number of scholars who argue that, in practice, these assumptions are not as essential as they might seem on a purely mathematical basis (cf. also Winer et al. 1991: 5). Second, it has even been claimed that there is no test that reliably identifies multivariate normal distributions (cf. Bortz 1999: 435). Third, the difference between discriminant analysis and CART is not just a statistical/mathematical one - rather, there is also a conceptual difference: while a discriminant analysis includes all variables simultaneously in the calculation to compute a prediction for one of the two constructional choices, the trees resulting from CART analyses include variables sequentially. For a native speaker, however, I believe that the model

116

MULT1KACTORIAL ANALYSIS IN CORPUS LINGUISTICS

underlying discriminant analyses is more realistic: it is intuitively more plausible to assume that all the variables' values/levels I have discussed are simultaneously available at the point of time the speaker chooses the word order rather than that the values/levels are included one by one sequentially. Moreover, while there is still considerable debate whether psycholinguistic theories of speech production should incorporate parallel or serial models of processing, I, following Berg (1998), consider parallel processing theories more rewarding. I have decided, for these reasons, to predict native speakers' choices with a discriminant analysis, which, as opposed to CART, comes closest to predicting choices on the basis of a simultaneous/parallel inclusion of the relevant data. Nevertheless, it might still be the case that these reasons do not satisfy my critics. I have, therefore, also analysed my data using the CART module of Statistica 5.5; the algorithms used therein are based on CART by Breiman et al. (1984), where CART and QUEST algorithms are used to classify and predict data in the absence of distributional assumptions. My CART analysis of the data was based on the parameters and settings listed in Table 6:36. The result of the analysis can be summarized as follows: as to classification, out of all 403 sentences, 351 (87.1 per cent) were classified correctly while 52 (12.9 per cent) were classified incorrectly, again a result that is extremely unlikely to be obtained randomly (according to an exact binomial test). However, we must also determine the prediction accuracy by crossvalidation. First, I used the split-sample technique analogous to the LDA, where T split the whole sample into a learning sample and a test sample three times; the samples and results are listed in Table 6:37. Second, on the basis of a 15-fold cross-validation, the technically most interesting statistic is the average misclassification cost which is .207 (SD = .02). As a rule of thumb, this figure can roughly be interpreted as the percentage of misclassifications in 15 splits of the sample into learning samples and test samples to be predicted given the respective learning samples. We may conclude that a reasonably good result has been obtained again. Admittedly, the cross-validated prediction accuracy of CART is not uniformly as high as the LDA results, but, apart from the test sample for oral data alone, it is still way better than what might be expected by pure chance. Moreover, there is a reason for these minor differences. Given the above Table 6:36 Parameters and settings of the CART analysis Parameter

Setting

Method Stop rule Prior probabilities Goodness-of-fit index

CART-style exhaustive search for uiiivariate splits FACT-style direct stopping fraction of objects = .5 identical: .5 for both constructions Gini measure

RESULTS AM) DISCUSSION

117

Table 6:37 Cross-validated prediction accuracies of CART for split samples Learning sample

200 spoken sentences and 150 written sentences 1 50 spoken sentences and 200 written sentences 174 spoken sentences and 176 written sentences Average

Test sample

Correct predictions for test samples

53 written sentences

81.1 % ***

53 spoken sentences

56.6% ns

26 spoken sentences and 27 written sentences

71.7%**

70% **

parameter settings, the CART technique does not utilize all variables for the prediction of a choice of construction but only the most important ones as determined by the analysis. Thus, for constructional choices where variables of an overall minor importance arc decisive, false predictions are more likely. As far as the importance of the individual variables of the Processing Hypothesis is concerned, the overall picture docs not differ strongly from the results of the discriminant analysis; for the sake of completeness, Figure 6:2 shows the results for the individual variables. Obviously, the overall picture has changed, but if we, as previously, investigate the ranking of the variable groups by comparing the median ranks of these groups in the discriminant analysis and in CART, then no

Predictor variable

Figure 6:2 Importance of predictor variables for CART

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differences emerge. In other words, although the data analysed in this study do not necessarily meet the mathematical criteria required by discriminant analyses, this technique can still be fruitfully applied to our research questions, and distribution-free techniques yield, lor all practical purposes, virtually identical results. This is not to say that discriminant analyses can always be successfully computed for such questions one should always decide that on a case-by-case basis after subjecting one's findings to non-parametric techniques as well. 6.4 Further evaluation To the reader not familiar with empirical investigations of this degree of complexity, the correlation coefficients and the prediction accuracies achieved might not seem as convincing as I portray them to be. However. I think this assumption would result from a serious misunderstanding as several things have to be borne in mind. We are dealing here with behavioural data which are influenced by a large number of factors and thus, on the whole, far from being straightforwardly predictable. In this particular analysis, the variables sometimes opcrationalize phenomena or aspects of human cognition (such as, e.g., activation) which are very difficult to measure directly; rendering it extremely difficult to achieve an even higher degree of variance explanation: the number of potential reasons for why some concept is more or less active than another one is so high that we often have to live with a certain degree of variance due to additional factors or idiosyncrasies on which no valid generalization can be based.31 What is more, in the majority of cases of particle placement, the two options (construction,, and construction,) are not mutually exclusive such that in each situation only one choice is theoretically possible (i.e. both constructions arc grammatical): note the fact that there is only a single nearly categorical rule governing speakers' choices, namely that pronominal direct objects require construction, (leaving aside the rare instances of contrastively stressed pronouns). Thus, in the vast majority of cases (namely in the about 81 per cent of non-pronominal objects), the phenomenon of particle placement theoretically leaves the speaker with both constructional options and the analyst with a situation where the choice of construction is determined by (i) the set of underlying cognitive preferences I aimed at investigating, (ii) idiosyncratic preferences, and (iii) other factors clouding the picture. In other words, the fact that in most cases the speaker can only rely on inaccessible cognitive preferences and idiosyncratic behavioural patterns rather than clear-cut grammatical rules corroborates my claim that my prediction accuracy concerning these inaccessible cognitive routines is indeed quite high. But is there any empirical evidence supporting this justification of the (already low) error rate? Let us briefly examine the constructions that the analysis failed to predict correctly by comparing them to the correctly predicted ones.3'1 Table 6:38 shows the means and standard deviations for the

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Table 6:38 Differences of average values for correct and false predictions Variable

AM (SD)for false predictions

AM(SD)far correct, predictions

LENGTHW LENGTHS TOPM AcrPC ConPC

2.3 (1) 3.8(2.1) .8(1.2} 3(4) 2.8 (2.8) O(.l)

2.9(2.7) 1,9 (5) 1.3(1.8) 4.3 (4.5) 3.5 (3.9) .1 (.4)

DlSFLUENCY

'lliM

df

P

3.03 2.75 2.31 2.30 1.73 1.68

210 179 101 82 94 211

.003 .007 .023 .024 .087 .094

interval variables for both correctly and falsely predicted choices of construction. What these values show is that the sentences that are predicted correctly have more active direct objects (since they were mentioned more often and closer to the VPC); additionally, there is a significant tendency for correctly predicted sentences to have longer direct objects - ConPC and DISFI.UF.NCY do not discriminate significantly between correct and wrong predictions. Likewise, if we test whether the quality of the prediction is crucially influenced by other (non-interval) variables, we find that false predictions are rare with idiomatic constructions and VPCs without determiners (especially pronominal ones) or with definite determiners. In sum, the prototypical instance of a wrongly predicted VPC has a short lexical direct object that is not very active and has most likely a literal meaning (no errors with idiomatic verb phrases and following directional PPs); some examples of such instances are given in (72). (72) a. ... before you put the next bit of a package containing a bandage off. b. East German security merely wanted to take away their identity cards, e. You'll open up the wound again. That is, the analysis fails to correctly predict mainly those instances of VPCs for which there is no obvious choice of construction since there are no definite rules governing these cases;*'' given the utterance and its discourse context, even native speakers would say that both constructions are possible and hardly differ in acceptability. In these cases, i.e. where the variables' values/levels do not make (strong) predictions, the decision for either construction is entirely due to the barely falsiliable semantic focus of the utterance (cf. examples (35) and (36) and n. 72), idiosyncratic behaviour of particular speakers or some unknown variables (whichever these may be). Since I would not wan! to rule out the possibility of additional variables, let us briefly look at two examples. One variable that might be relevant is concerned with the priming of syntactic structures. In several analyses (of, e.g., transitive clauses and dative

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structures), Bock (1986) has demonstrated that subjects' choices of construction can be manipulated by exposing the subjects to sentence structures prior to their decision for a particular constituent ordering. In my data I tested whether structural priming has influenced the speaker's/ writer's decision for a construction by counting whether the analysed VPC was preceded by another VPC in the preceding three clauses and, if so, whether the two VPGs had the same word order or no(. Consider Table 6:39. Obviously, structural priming is irrelevant in the vast majority of cases (333 out of 403, i.e. about 83 per cent), where no VPC precedes another one within a window of three clauses. If, however, the VPC under investigation is preceded by another one (70 cases), then in the bold printed 53 cases (75.7 per cent) the constructions arc identical. The distribution of constructions in the partial table of (24 + 46 =) 70 cases where structural priming makes a prediction is highly significant (x"(l) = 15.24; pVPart [N1,]]]= construction.

Table 6:39 The effect of structural priming on particle placement Prediction following from structural priming Construction/, Construction/

Row totals

No prediction

Observed in Construction,, 16(66.7%) 9(19.6%) 169(50.8%) 194(48.1%) the data Construction, 8(33.3%) 37(80.4%) 164(49.3%) 209(51.9%) Column totals 24 46 333 103

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121

In order to assess the influence of ideal syllable structure, one needs to test how the choice of construction affects the transition of segments. Since the different orderings of post-verbal elements do not alter the constituents themselves, we only need to investigate the three segment transitions between the constituents that may be placed in alternative ways in which each constructional choice results. These transitions are accordingly marked in (74). (74) a. John picked |, up | , the book | , from thefloor.= construction,, h. John picked , the book |., up j , from the floor. = construction.

In (74a), the transitions one, iwo and three can be characterized as GV ([piktAp]), CC l'[Ap8aJ) and CC ([bukfrtmij) respectively; in (74b), on the other hand, the transitions one, two and three are CC, GV and CC respectively. In other words, when we focus on segment transitions only, no word order in (74) is superior to the other as both feature iwo non-optimal transitions and only one optimal CV transition. Let us now turn to the authentic data analysed so far. Assuming that this variable is, if at all, more likely to be influential in oral language, I have investigated all 200 examples of the spoken data and compared the segment transitions resulting from the actual choice of the speaker to the segment transitions resulting from the other construction. If there is indeed a tendency lo avoid CC transitions, then the constructions chosen by the speakers should exhibit significantly less CC transitions than those not chosen by the speaker. However, the data show absolutely no tendency in this direction, as is shown in Table 6:40, where '' compare the numbers of CC transitions in the chosen and not-chosen construction. In most cases (89 per cent), both word orders result in identical segment transitions. What is more, the number of cases where the construction produced by the speaker has a higher number of optimal transitions ('chosen > not chosen') is outweighed by an equal number of cases where we find the exact opposite ('chosen < not chosen'). In other words, in the present data, segment transitions do not play a role whatsoever. In sum, while I am the first to admit that other variables may very well play a role in determining speakers choices (especially since this is the first comprehensive multifactorial analysis of particle placement), it is obvious that such a stipulation needs to be supported by empirical data. For one variable (structural priming), such evidence was provided, for another Table 6:40 Comparison of chosen vs. non-chosen constructions (segment transitions) Chosen > not chosen

Chosen = not chosen

Chosen < not chosen

Row totals

11(5.5%)

178(89%)

11(5.5%)

200

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MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

candidate (segment transitions), no such effect could be found. Therefore, given both the exceptional prediction accuracy as well as the fact that my hypothesis accounts for all relevant variables, the burden of proof lies with those researchers postulating the effect of such additional variables and, more importantly, they would also have to develop a theory that can incorporate all variables found to be affecting particle placement. As long as such empirical evidence, let alone a more comprehensive theory, does not exist, I take it that my approach is well supported. Finally a sceptic might argue that 83.9 per cent is not a very convincing result. He might argue that while we have seen previously that 338 correct predictions out of 403 trials is very unlikely to be obtained by chance, even an uninformed linguist would, on average, already achieve a prediction accuracy of 59.55 per cent: he knows that speakers would correctly choose a pronoun in almost all cases of pronominal direct objects (7 7 cases out of 403) and. from the remaining 326 cases, he could simply guess 50 per cent of the cases correctly (since there are two possible constructions to choose from), i.e. 163 instances. Thus, the average uninformed analyst would, on the basis of a single rule (pronouns require construction,) and pure chance, already predict 77 + 163 = 240 cases out of 403 correctly, the 59.55 per cent just mentioned. Therefore, it might seem as if 83.9 per cent is not really a result worth mentioning. However, this conclusion is faulty. As was just said, the competing uninformed linguist has to choose a construction in 403 - 77 = 326 cases (omitting the virtually obligatory cases of construction] with pronominal direct objects). My analysis predicted the choice of construction correctly in 338 cases - but these 338 cases include the 77 cases with pronominal objects where there is no real choice so they need to be left out in the evaluation of my analysis since it is no great achievement of mine to predict these cases. That is, my analysis made 338 — 77 = 261 correct predictions for a construction where there was a choice. Therefore, we have to find out how high the probability is that uninformed analysts can correcdy guess the choice of construction 261 times or more in the 326 cases where there is a choice (261 -I- 65) just by chance. According to the exact binomial test, this probability approaches zero (2.86-10"™). This line of reasoning is summarized in Figure 6:3. It is still conceivable that linguists can achieve predictions rates that go beyond 59.55 per cent on the basis of their knowledge of the literature prior to my study - however, we have seen that they would then face the difficulty of not knowing how important each variable is, which in turn has only led to concessions of 'truly complicated array of facts' rather than more sophisticated analytical techniques. Likewise, it might be possible that native speakers could also achieve good prediction rates when they try to predict which construction another native speaker will choose even then we would only know which rate speakers can achieve, but we would not know how they do it as they cannot tell us in any scientifically valid way. To sum up, I am convinced that neither native speakers nor informed

RESULTS AND DISCUSSION correctly predicted (338)

pronouns (77) no choice

correctly predicted utterances where there was a choice (261)

123

falsely predicted (65)

falsely predicted where there was a choice (65)

Figure 6:3 Distribution of construction predictions relative to kinds of direct objects linguists would achieve such a high degree of accuracy in predicting other native speakers' decisions on the basis of the results of previous studies since the only virtually obligatory rule cannot guide speakers' or analysts' decisions in the majority of cases. Given these particular characteristics of particle placement (the non-exclusiveness of the two constructions in most cases and the large number of variables that needs to be considered) and comparable results from other behavioural disciplines, I am convinced that 84 per cent prediction accuracy is an exceptional result. In this connection, let me briefly speculate about the relevance of a variable that could not be investigated with the corpus data used here, namely the (mostly contrastive) stress on the direct object (STRESS). On the basis of the previous results we can estimate fairly well what findings we would have made. STRESS is not indicated in written data and probably not often found in oral data (just like DISFLUKNCY). Thus, we would expect a fairly low correlation coefficient (in the direction unanimously mentioned in the literature) ior the whole of the data and a slightly higher one for the subset of oral data. When it comes to pair-wise oppositions, however, we would probably find that STRESS is the strongest variable of all since (as was argued in the literature) it can at times even overrule TYPE: pronoun. Given the expected limited frequency of (contrastively) stressed expressions in the corpus data, however, STRESS would not receive a high factor loading, but would probably increase the prediction accuracy at least slightly. Two final problems need to be addressed. First, one question unfortunately remains unanswered. While the Processing Hypothesis receives unanimous support, it was deliberately formulated in such a way as to include the processing effort of both speaker and listener. Now it would be desirable to find out which of these two perspectives on processing is more relevant for particle placement. This, however, is not possible here since nearly all the variables we have shown to be relevant can exert their influence on both the speaker's processing cost and the (speaker's assumptions about the) hearer's processing cost. Consider, e.g., discourse-functional determinants of processing:

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MULTTPACTORIAL ANALYSIS IN CORPUS LINGUISTICS

• postponing new objects to sentence-final position facilitates processing for the hearer since the newest information is given only after all other information has been provided; • postponing new objects to sentence-final position facilitates processing for the speaker since the speaker affords himself more time to plan and produce cognitively demanding parts of the utterance (cf. Wasow 1997a, b, as well as Arnold et al. 2000: 33 and the references cited therein). The same holds for morphosyntactic variables: • postponing long and complex objects to sentence-final position facilitates processing for the hearer since the most difficult structural constituents are expressed only after all other information relevant for the parse has been provided; • postponing long and complex objects to sentence-final position facilitates processing for the speaker since the speaker affords himself more time to plan and produce the most demanding structural parts of the utterance (cf. Arnold et al. 2000: 32). Thus, on the basis of my own analysis and the experimental data discussed in Arnold et al., it seems impossible at present to decide whether processing is more important by virtue of its cost for the speaker or for the hearer: very often, both perspectives make identical distributional predictions. It is interesting to note, however, that although my study has not investigated processing in real time experimentally, the methodology nevertheless yields very similar results. In other words, provided that the methodology is sophisticated enough, corpus analyses allow for interesting insights into linguistic processing as well. Second, let me also anticipate another objection. The statistical techniques used so far make it difficult to choose among competing hypotheses that make identical claims as to the strength and direction of the relationship between the independent variables and particle placement. Yet, this valid objection is much less of a problem than it might look at first: it holds for nearly every analysis using inferential statistics. Whenever a researcher has some hypothesis (H,), analyses some data using inferential statistics and obtains a result supporting his H,, it is, with hindsight, possible to argue that the researcher's H, is not the correct one, but some other H, that is equally compatible with the data. That is. if we take this to the extreme, it would not be possible to ever accept some H, as there are, at least theoretically, always competing alternative hypotheses that could be responsible for the outcome of the investigation and cannot be ruled out because they make fully identical predictions. Strictly speaking, one might even say that this dilemma is inbuilt in the paradigm of falsificatory testing: since we only attempt to falsify H0, the logical counterpart of our own hypothesis, a result conforming to our hypothesis only rules out H0 but does not pick out one among many particular alternative hypotheses making identical predictions. Never-

RESULTS AND DISCUSSION

125

theless, it is common practice to accept one's own hypothesis if the corresponding null hypothesis is not supported significantly by the data, and in this study, I simply follow this generally acknowledged practice.3" Apart from this general comment, we should also briefly return to the advantages of the present analysis. Until now, only a handful of studies have attempted to unify the distinct findings concerning particle placement - the majority of analyses did not even recognize various variables already discussed in traditional grammarians' works. The Processing Hypothesis readily incorporates all the relevant variables and excludes all the irrelevant ones. Given the fact that in more than 100 years of research not a single analysis has managed anything only slightly similar (as I have shown previously, there was not even a single descriptively adequate analysis) or has been subjected similarly successfully to prediction tests with hundreds of examples, I would not hesitate to place the burden of proof on the linguist who, after having seen my results, postulates the existence of another hypothesis that has the same or even better descriptive, explanatory and predictive power; Chapter 8, however, will elaborate on such an alternative approach. The following chapter will discuss some general implications of the present study, i.e. consequences that go beyond particle placement proper and are concerned with how linguistic categories are conceptualized, what kind of explanation we seek for our findings and how these results relate to other research on linguistic structure. Notes 1 Note that, in order to test conservatively; all significanre tests are two-tailed even if previous hypotheses/results would also have allowed for one-tailed tests. 2 Here it becomes obvious why the labels construction// and construction/ are necessary even if continuous construction and discontinuous construction are more mnemonic (ef. n. 5): without such an 'at first glance counter-intuitive labelling, the interpretation of all the correlation coefficients would not be possible without further explanations. 3 At this point, it is worth addressing some objections that are likely to be raised. 1'rom time to time, methodological criticism has been levelled at quantitative statistical analyses oflinguistic data. In a more recent study, Givon (1992a) has addressed potential problems of using Chi-squarc analyses and the correlations deriving from them. Using data from text-distributional analyses of word order in several languages, he has shown that interpreting contingency tables and their Chi-squarc values can he complicated or distorted by the fact, that the 'Chi-square test for correlations is blind to skewed directionality' (Givon 1992a: 308): this potential flaw has been briefly mentioned in this study, too, cf. section 2.6.2. While this is in principle correct, it is not a problem underlying statistical analyses in general or Chi-squarc analyses in particular rather, a careful analysis can easily account lor skewed directionality by (i) testing the relation found for significance using the so-called contributions to Chi-square (cf. Zofcl 1992: 187) or by doing a configural frequency analysis (cf. Bortz et at., 1990: 155(T.) and (ii) observing the relation between

126

4

5

6

7

8

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS observed and expected values as will be done later. In this respect, therefore, Givon's criticism of the Chi-square test and the resulting correlation coefficients docs not apply to the computations and their subsequent interpretations in the study at hand. In this respect, it should be noted that, although Givon argues for the use of 'some reasonable statistics' (1992a: 317; a line of reasoning to which I would also subscribe), his own paper does nevertheless fail to economically utilize available statistical techniques. His claim that the skewed correlation between OV word order and the function-class definite in Mandarin is mediated by Y-movement could have been tested more easily, more economically and in a fairly foolproof way using the technique of partial correlation introduced above (cf. Chapter 2, n. 25). In this sense, Givon's evaluation suffers from an admittedly only minor methodological lapse similar to the ones he himself criticizes in the course of his paper. The values given here as expected frequencies are rounded integers. However, for reasons of accuracy, the calculation of Chi-square as well as the contributions to Chi-square are based on the exact non-rounded values. This holds for all the cross-tabulation tables to be discussed. There are various coefficients of correlation for contingency tables of nominal variables that are mentioned in the literature; the ones most frequently found are probably <j>, Cramer's index ('s generalization to k X m tables) and the contingency coefficient C. Unfortunately, they arc all problematic to some extent. For example, it is not always possible to compare the distributions in two tables using these coefficients of correlation since the range of values that these coefficients can take on differs across tables. While Cramer's index would be well suited for comparisons to Pearson's r (both can be derived from the General Linear Model), I have nevertheless chosen to use the correlation coefficient A.. While it is much less frequently found than other coefficients of correlation, it suits our purposes ideally since it assesses the percentage our prediction accuracy increases when the independent variable is known rather than just quantifying something as abstract as shared variance. The notion of interaction is defined as an effect that goes beyond the main effects (i.e. those of single variables) and can only be explained with reference to a particular combination of levels of several variables (cf. Bortz and Doring 1995: 617; Bortz 1999: 285). For this study this means to test whether, e.g., some variables are influential only in oral language or in written language. Since both Lcvene's test and Brown and Forsythe's test were statistically highly significant, the homogeneity of variances assumption of the commonly used t test was violated, which is why Welch's t-tes.1 was computed here and in the remainder of this study. However, in the interest of readability I decided to limit the number of statistics in the following text by omitting the results of the / tests since they do not differ from the result of the correlational analysis. The r2 value is the most relevant one here because it denotes the percentage of common variance of the two variables. Put diffcrcndy, r" denotes the proportional reduction of error in predicting the value of the dependent variable when we know the independent variable's value. It is, thus, comparable to A,; cf. above. In what follows, thus, only r2 will be given.

RKSULTS AND DISCUSSION

127

9 For this cross-tabulation and all following ones of interval/ratio variables, no column percentages will be displayed for expository reasons. A further comment is necessary concerning the use of the notation 8+ in this table and following ones (the notation means 'eight words and longer'). It is of course possible to use the formation of such groups in these tables quite misleadingly: in Chen's (1986) study, e.g., the empirical results for individual values are often summarized using such groups of values. For instance, in the presentation of the results for length Chen divides the data 'into five groups according to the number of their syllables' (1986: 86), namely 1 2, 3-5', 6-10, 11 15, \6+. No reason is given why this was done or why this classification (five groups with the group sizes as just given) was used rather than other equally possible ones (e.g., 1—3, 4-6, 7 9, 10—15, 16+). Even more noteworthy is his discussion of distance to last mention for short objects, where an even more extreme classification has been chosen (1 2, 3-5, 6-20 [!], 21+). It is therefore quite possible that Chen's arbitrary classification procedures mask results by accident, especially since, as we will see later, results can be extremely complex at times so that one must be careful not to subsume many different results under a single group. In this study we will consider such larger groupings of values for expositoryreasons only (otherwise, the resulting table would be too large), and, what is more important, groups will be formed only if all the individual values constituting the group behave identically (i.e. have a preference for the same construction). Thus, no information is accidentally omitted. 10 These results might appear strange since, e.g., semi-pronominal nouns intuitively seem to have a strong preference for construction,, 10 out of 13 occurring in construction |. However, the contingency table shows that the knowledge of whether the direct object is semi-pronominal or not does not improve our prediction of the construction the speaker will choose for this direct object: we would always predict construction,, which occurs in 10 out of 13 and 199 (77 + 115 + 7) out of 390 cases respectively. Thus, this variable cannot improve our prediction accuracy. Moreover, if the distribution 3 vs. 10 is subjected to a binomial test with 48.14 per cent (the row total) as probability of success, then the result turns out to be insignificant, too: p lnl ,,, m i a i „,, = .061. These remarks equally pertain to proper names as direct objects. 11 At first glance, this correlation coefficient for pronominal direct objects might also seem suspicious: on the one hand, pronouns exclusively occur in construction!, so the variable is extremely powerful; on the other hand, however, the coefficient for pronominal objects is lower than the one of, say. NPs of intermediate complexity, which do not have such an unanimous effect on the choice of construction. This paradox is due to mathematical reasons, and we will return to some more interesting implications later. 12 This might be indicative of the fact that the database investigated here is indeed (i) fully representative and (ii) sufficiently large as the findings by Biber et al. (1999) are based on the evaluation of the Longman Spoken and Written English Corpus (I,SWE) with about 40 million words. 13 This result is in a way quite astounding since correlations between definite determiners and givcnness (i.e. little processing cost) have been found in a large number of studies. Therefore, one might also have expected to find a correlation between definite determiners (indicating the givenness of the direct object's referent) and particle placement. Thus, I wanted to find out what is responsible for this finding.

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MULTIFACTORIAL ANALYSIS IX CORPUS LINGUISTICS

To my surprise, 1 found that definite determiners occurred more often in cases where the referent of the direct object had not been mentioned before (98 cases) than in cases where the referent had been mentioned before (77 cases). The average length of these 98 discourse-new definite direct objects (5.7 syllables; SD: 4.6), however, was significantly longer than that of the 77 discourse-familiardefinite direct objects (3.8 syllables; SD: 2.7) and nearly significantly longer than all 175 definite direct objects (4.9 syllables; SD: 4.0). From this it follows that speakers do indeed use definite determiners for discourse-new referents, but if they do, then they provide additional information about them with extra modification. For instance, the sentence I was running into the man who came out of the grocery store is perfectly acceptable even if the man has never been mentioned before. An analogous sentence in the corpus data is The commission has also thrown out the idea, put forward by some industrialists and businessmen, that special provision is needed for computer fraud: the referent of the direct object has not been mentioned before, but is so extensively modified that the use of the definite determiner is still licensed. In other words, the fact that definite determiners do not correlate with particle placement is simply due to the fact that the correlation between definiteriess and givcnness is, in my corpus data, not that strong. 14 Another technique, which is less advanced but easier to understand, yields the same results: if we take out this influence of pronouns by looking only at the correlation between non-pronominal direct objects with and without determiners and the choice of construction, then no determiner again has no further influence on particle placement: X = 0; p = 1. 15 Note that LM is identical to the independent variable in Peters (1999, 2001). For Petcrs's analysis, however, I have shown that her data, albeit significantly deviating from a chance distribution, do not allow a prediction of the choice of construction (X = 0). The data in Table 6:11, on the other hand, do allow us to predict constructional choices since no construction is uniformly preferred across all conditions. 16 Recall that this variable was receded to avoid missing data; cf. Chapter 5, n. 12. Thus, large/small values indicate small/long distances to the last mention respectively, and 0 stands for no prior mention. 17 The standardized betas for TOPM and OM are .4 (p3

feedback to the word level and will again reinforce the activation ofJohn,, while also partially activating phonologically similar words like gone. At the point of time the system"' inspects the activation levels of all nouns currently activated (because the current syntactic node 1 ' is an NP and no production of a determiner has been 'scheduled'), it selects the most highly activated node representing a noun and integrates it into the N and NP slot currently processed at the syntactic level. Put differently, 'selection entails the linkage of a word to a slot in a syntactic frame' (Dell et al. 1997: 806); cf. Figure 8:3. After this selection of the noun and its integration into the syntactic frame, the production processes at the lower levels (word/lexical level, phonological level, etc.) proceed without further delay ('[e]xecution at the syntactic level entails immediate execution at all lower levels, leading directly and immediately to motor encoding' (Stembergcr 1985: 150)). Upon this, the word node provides feedback activation to the syntactic level so that the NP node self-inhibits and the next syntactic node (VP) can be activated in due course. Note also that, during all of these processes, the system already processes the upcoming items on the conceptual and syntactic levels. The process of selecting the verb proceeds in a similar manner to that of selecting the sentential subject. Let us lirsl look at this process in general before we tie it to our example John picked up books/John puked books up. In general, after the selection of the NP node the syntactic VP node of the verb becomes the current node and several lexical verb nodes (representing verbs sufficiently compatible with the intended meaning) compete until one of them gets selected (i.e. linked to the appropriate syntactic slot. i.e. Y in the VP) and scheduled for production. In our case, however, verb selection is more complicated. First, the choice of the syntactic configuration (e.g. a transitive sentence I John lifted books], construction,, [John picked up booh] and construction, [John picked books up\) is highly related to the choice of the verb. In the words of Dell (1986: 316):

Figure 8:3 Step 3 of the generation of the utterance John picked up books/ books up

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MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

Although it is clear that semantic and pragmatic considerations should guide the syntax, it is less clear that the activation levels of word nodes should, as well. There arc, however, some experimental findings that indicate that the retricvability of a particular word affects the syntactic structure of the spoken sentence (sec Bock 1982 for review). To model these effects, the activation levels of word nodes will have to be taken into account by the syntax.

In our case, if the nodes for the three possible syntactic configurations from above were partially active and the verb node for lift was activated most, then this should raise the probability of the transitive configuration to be selected over the VPCs (barring, for the sake of the argument, the lift of lift up}. If, however, upon a high degree of activation of pick,, pick is selected and scheduled for output, the system must also decide on a syntactic configuration of the VP. It is here where it becomes essential to examine the time course of activation precisely. Consider Figure 8:4. When VP is the current node on the syntactic level, several things happen. On the lexical/word level, activation summates in pick, until it becomes selected; upon selection, pick, sends feedback activation upwards to the V node in the VP and sends activation downwards to initiate phonological and motor encoding. Also, the upcoming concepts (e.g. hook and up) start activating their lexical nodes (i.e. book, and up, respectively). In the next step (i.e. during the summation of activation in phoneme nodes for pick), activation also summates in the nodes books, and up,. Whichever node accumulates enough activation until the activation levels arc inspected, gets selected. Assuming that books,, was selected, the node books, sends out (i) activation to the phonemic level to instigate motor encoding, and (ii) feedback activation to the VP where the noun precedes the particle, i.e. the currently partially activated node of construction,. It is this feedback from the selected lexical node that determines which constructional node is activated (cf. Dell's (1986) arguments quoted previously at length). That is, if book, has been selected, feedback activation can spread up to the N. NP and VP nodes of construction,, but not to the VP

Figure 8:4 Step 4 of the generation of die utterance John picked up books/ books up

THE ACTIVATION OF CONSTRUCTIONS

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Figure 8:5 Step 5 of the generation of the utterance John picked books i

node of construction,,, because the particle of construction,, does not receive activation (cf. Figure 8:5). If the system selects a morphologically complex TPV to begin the VP. then some mechanism must be incorporated into the theory in order not to 'forget to insert the particle' at a later stage. This is additional prima facie evidence to stipulate constructional nodes (cf. n. 9): if it were not constructional nodes that, in our example of construction,, 'bear in mind' that a particle is yet to follow, one would have to make up some other device serving that purpose (such as morphological integration nodes [MI nodes]; cf. also section 8.2.5 for empirical evidence strongly supporting this hypothesis). With the present approach, the feedback from the lexical level to the constructional level decides on a construction and 'remembers' the particle; cf. the arrows pointing downwards to Pit of construction, in Figure 8:5. After the selection of the verb and the following constituent (e.g. books,), the system proceeds with the VP-internal structure in a way similar to that outlined above for the subject and verb. Since the VP node representing construction, has already received feedback from the NP node, the final node of this VP to be activated and related to a lexical node is that of the particle. Again, several lexical nodes (particles) will simultaneously compete with each other, and the one having the highest degree of activation at the point of selection will win out and become selected; cf. Figure 8:6. This process is reiterated similarly for all remaining words.18 While the preceding discussion has already explained lAMs with respect to particle placement, one still needs to show that the variables' values that we have found to correlate with construction,, and construction, also correlate with high degrees of activation of the nodes of the particle and the direct object respectively and the corresponding constructional nodes/syntactic frames. Obviously, we are not in a position to measure the degree of activation of nodes, which is unobscrvable. However, if other studies provided independent evidence for the degree of activation of some constituent in some discourse situation in the direction we have observed in the data, then the 1AM provides an independent \\o\\-ad-hoc explanation of particle

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Figure 8:6 Step 6 of the generation of the utterance John picked books up placement. For instance, we have seen in section 6.1.2 that previously mentioned referents of direct objects correlate with construction,. If it. was possible to show that such referents are highly activated for reasons independent of the principles governing particle placement, then we would have a non-circular argument in favour of integrating this variable into the IAM analysis proposed here. In the following sections, I will show how the variables' influence on particle placement can be explained in the IAM outlined above. 8.2 The relation of variables to activation 8.2.1 Discourse-functional variables The discourse-functional variables concerning the preceding discourse are very straightforwardly related to matters of activation.19 As to the variable LM, if the referent of the direct object has been mentioned in the preceding discourse, the node for this referent will be more active than a referent that has not been mentioned previously. This follows logically from properties of lAMs mentioned previously, namely the fact that the degree of activation A of a node Y at time step t A (Y, t) is a function of Y's previous level of activation. When the node representing a particular referent has been activated during the previous discourse, its activation will, after the short phase of self-inhibition, be above resting level (hyperexcitability phase) and thus be more likely to be activated again as compared to cases where there was no previous mention of this referent. Thus, according to the activation model, we would expect to find previously mentioned referents more often in construction,. This is exactly what we have found, so our prediction about the role of LM in terms of activation is borne out. The line of reasoning for the other functional variables concerning the preceding context is virtually identical. If the referent of the direct object has been mentioned very often before (i.e. high values of TOPM), then it is more highly activated (and/or easier to activate again) for the reasons just given and, according to the IAM, more likely to be produced early. Again,

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this prediction is supported by our corpus data. The same explanation can naturally be extended lo AcrPC: short distances between the last mention of the direct object's referent and the VPC (i.e. high values of AcrPC) should result in a higher activation of the corresponding node since the node's activation has riot yet completely fallen back to the resting level. This, in turn, leads us to expect the preference for construction, we found. Finally, let us turn to CoHPC. In this case, no direct reference to the direct object's reference is necessary, so how do we relate this aspect of CoHPC to activation? In lAMs, a semantic concept docs not only activate a single word node; rather, a single semantic concept activates several conceptually related words simultaneously (cf. above). For instance, a semantic concept X activates the lexical node of X and the nodes of hypcronyms, hyponyms. homonyms of X and other semantically related words. Thus, even if a particular word node has not been activated by the corresponding semantic concept, this word node might nevertheless be active due to the excitatory activation it has received from, say, one of its hyperonyms. In that case, it would be cohesive to the preceding discourse, so construction, would be expected; since this is supported by the above results, the activation account is borne out. In sum, all of the discourse-functional variables concerning the preceding context investigated in the previous chapters and their relation to particle placement can be explained with reference to lAMs. Given that the time course of activation (including self-inhibition and hypcrexcitability) has been motivated before independently, the IAM account of particle placement presented in section 8.1 receives independent empirical support. As to the variables concerned with the subsequent context, they cannot be related to the activation processes occurring during the selection of the VPC since their values/levels occur temporally after the choice of construction has been made, i.e. when all activation processes determining the choice of construction have ceased. Put differently, according to the activation model proposed here, no effect of these variables is to be expected; this claim was clearly borne out by the data; cf. section 6.1.4. This last point is of particular importance as it shows that the activation model cannot only explain why the relevant discourse-functional variables have the impact on the choice of construction they have, it can also be used to predict which of the variables suggested in the literature should not have an influence on the choice of construction.

8.2.2 Semantic variables The semantic variables' impact on particle placement can also be connected to activation in an attractive way that is supported by several observations independent of the current focus of interest. It was already mentioned in the introductory section on lAMs that complex forms pose a particular challenge to lAMs. The ensuing discussion starts out from the variable IDIOMATICITY, which is strongly connected to the issue of complex forms.

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On the basis of speech error evidence, lAMs were argued to represent knowledge of complex forms in a way that is, at least from the point of view of parsimony, not ideal: morphologically complex forms (such as idiomatic expressions) that can be analysed as being formed on the basis of a regular rule are nevertheless listed as individual complex forms, thereby introducing an element of redundancy (cf. Stemberger 1985: 172-7). These forms arc acquired as lexical units (form-meaning pairing) and given an analytic representation. For production, the conceptual node activates the lexical unit node for the morphologically complex form, which in turn jointly activates the component parts/nodes into which it can be formally analysed. There is some experimental evidence from studies on Dutch particle verbs that seems to support this approach: Schreuder et al. (1990) found that the mental lexicon of Dutch native speakers seems to have a full listing of decomposed entries for separable particle verbs, i.e. separate entries for verbs and particles (together with MI nodes linking both ol them whenever they can form a separable complex verb). But how exactly does this relate to particle placement in English? The meanings of idiomatic TPVs are not compositional: the verb and the particle do not each contribute an independent component to the overall meaning of the VFC, although an analysed representation of the phrasal verb might also be available. Therefore, both the form and the meaning of the idiomatic TPV are acquired and stored together as a unit, which in turn entails that once such a verb is needed for an utterance, it will be accessed as a whole along Sternberger's lines: the conceptual nodes activate several syntactic configurations compatible with the content to be conveyed as well as the lexical unit node representing the idiomatic phrasal verb (e.g. eke out L ), which, in turn, activates the syntactic component parts (V and Prt, that is) it requires. The factor that is responsible for the close relation between idiomatic TPVs and construction,, is that the idiomatic phrasal verb is stored as a unit and, thus, docs not favour independent access of its two component parts: when the lexical unit node is activated, its component part nodes receive a stronger jolt of activation than the component parts of compositional TPVs. The justification for assuming that this jolt of activation is stronger is their unit status, i.e. the fact that the non-compositional semantics of the TPV have lead to the storage of a complex form. This in turn motivates the quasi-joint selection of the two lexical items and results in strong feedback activation of the syntactic slots V and Prt respectively; as a result, construction,, is produced. With literal phrasal verbs, the situation is somewhat different. As was already discussed in section 4.2, the meaning of literal TPVs is compositional and commonly denotes a process where the referent of the direct object undergoes a movement process, the path or end of which is denoted by the particle. In Talmy's (1985) terminology, the action denoted is decomposed into the meaning contribution of the verbal nucleus and the additional semantic feature contributed by the particle (cf. Chapter 4, n. 8). In other words, while literal TPVs may well also have a node for the

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complex form (cf. the representations above), the fact that both meaning components are easily isolable licenses separate access of both verb and particle. Put differently, the particle can be accessed separately for the production of an utterance and is, as a separate lexical entry with its own semantic contribution, not so much constrained by the degree of activation of the verb with which it happens to make up a TPV Thus, with literal and metaphorical phrasal verbs the IAM predicts that there will be no strong preference for construction,, (at least on the grounds of the variable IDIOMATICITY), which is what we found. If the above analysis of the production of TPVs is correct, what implications would that have for comprehension? The processes would have to work the other way round: the listener hears the verb, upon which all verb nodes with this verb (simplex, phrasal, prepositional, phrasal-prepositional, etc.) are (partially) activated. There is indeed some experimental evidence for this process: in experiments with German native speakers, Hillert (1998) found that verb stems (such as geben) which can be continued as several different separable particle verbs (e.g., idiomatic: aujgeben vs. literal: weitergeben) prime both verb meanings irrespective of the immediate context, supporting the redundant representations of complex verbs assumed by Stemberger and the activation processes described previously. Thus, the analysis of the production of idiomatic TPVs receives some independent evidence from comprehension studies. Let us look at CONCRETE. Concrete referents tend to occur in construction, (i.e. early in the sentence), whereas abstract nouns tend to occur in construction,! (i.e. late). In order to integrate this finding of the present study into the IAM analysis, we need to show and explain this tendency on independent grounds. To that end, recall that, in lAMs linguistic elements are argued to occur early in sentences if their level of activation A (Y, t) is high enough to be selected early, while linguistic elements occurring late(r) arc less active at early stages of production of an utterance. Since A (Y, t) is determined by A (Y. t-x) or Wx, we need to show that there is reason to assume that, with referents of concrete nodes, cither A (Y, t-x) or Wx is generally higher, thereby leading to an increase of A (Y, t) at the point of time t when the system inspects the activation levels of nodes for selection. One reason is that concrete concepts are. in general at least, acquired earlier as well as manipulated and spoken about more often (entailing their nodes and the links connecting to these nodes are activated more frequently), thereby increasing their resting levels. This increase of the resting level in turn results in their need of less activation in order to reach a level high enough to be eligible for selection. The converse holds for abstract referents: they are acquired later, spoken about less often and never manipulated, so their nodes are active less often and their resting levels are not. likely to facilitate high activation levels. There is some experimental evidence supporting this way of treating CONCRETE in lAMs. As was briefly mentioned in section 4.2, Bock (1982: 17, 20—1) summarizes a number of experimental studies showing convincingly

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that concrete referents of" words as well as sentences containing concrete referents are recalled more often and more reliably than abstract referents. Since, in IAMs, recall of referents corresponds to repeated activation of nodes representing these referents, these findings support the claim that the resting levels of nodes of concrete referents are in general higher, thereby facilitating activation (and early production). Since this is exactly what the IAM account of CONCRETE needed as support, the interpretation of my findings appears strongly supported on independent grounds.2" Finally, let us turn to ANIMACY. In principle, ANIMAOY can be related to activation along the same lines as CONCRETE. On the basis of the findings reported in Bock (1982: 17, 20-3), one might suggest that animate referents are easier to retrieve and, thus, easier to activate than inanimate referents. However, while this is inherently plausible, the empirical findings suggest otherwise: ANIMAOY had only a very limited influence on particle placement (cf. Table 6:10). Nevertheless, this need not be taken as evidence against the activation model: the limited impact of ANIMACY on particle placement is probably due to the fact that ANIMACY influences phrase order only in conjunction with changes in the semantic role configuration (cf. McDonald et al. 1993: 202) and, thus, not in the case of particle placement.

8.2.3 Morphosyntactic variables Among the most powerful variables are LENGTH\V, LENGTHS and COMPLEX. I see two ways in which they can be related to activation. First, these morphosyntactic variables are related to previously discussed discoursefunctional variables along the lines discussed in section 4.3. Information that has been mentioned more or less often is related to activation levels in a way argued for in section 8.2.1 and is in general encoded with a larger or smaller amount of linguistic material respectively. This is also the case in the present data set: the correlations between LENGTHS, LENGTHW and COMPLEX on the one hand and LM, AcTPC. TOPM and CoaPC on the other are negative and highly significant without a single exception. According to this argument, there is, therefore, at least an indirect relation between lengths and complexity and particle placement - however, one might raise the question whether this is sufficient to motivate the strong correlations we have found and I am in fact not completely sure it does. Consequently, there could also be another explanation in terms of activation. Let us return to the process of producing an utterance after the selection of the verb, i.e. Figure 8:4. The concepts to be referred to in the remainder of the utterance compete for the next syntactic slot. Given the input from the conceptual system, the activation of the lexical nodes of the particle and the elements making up the direct object noun phrase summate. As the activation levels of these lexical nodes increase, so do the corresponding constructional nodes on the syntactic level (by spreading activation). But

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while activation of the syntactic node of the particle can summate fast (since (here is only a single lexical node for the particle), the activation level of the syntactic NP node may behave differently. If the NP is very long and complex, then all the lexical concepts belonging to the direct object NP arc activated sequentially. It will, thus, take longer to activate the syntactic NP node sufficiently for selection (as more information needs to be integrated into an increasingly complex direct object NP) than ii would take if the direct object would be very short and simple (e.g. one word), where the lexical node can immediately increase the activation level of a single NP node. In the words of Bock, 'representations with less information will finish the retrieval process faster' (1982: 31). That is, '[i]f some subcomponent of the interfacing representation is entered into lexical processing before other subcomponents, so that its semantic and phonological processing will tend to be completed earlier, its associated syntactic productions will also be activated earlier' (Bock 1982: 30). This lies in with some of the observations made in Chapter 6. For instance, construction,, is already preferred with direct objects consisting of more than two words. One might wonder whether the activation model would not predict that particles precede the direct objects even if the latter consist of only two words. But a closer look at the 132 cases of two-word direct object NPs shows that nearly 90 per cent of them consist of a noun preceded by a determiner. These determiners are. as function words and given their overall frequency in virtually every piece of discourse, very easily activated and, additionally, two-word NPs are obviously not very complex so no construction and activation of syntactic constituents within the direct object NP. which might delay the overall increase in activation, are necessary. Once, however, the NP becomes more complex such that additional modification leads to an increase of content words or even of syntactic nodes ((^constructions or embedded clauses), construe tion0 is strongly preferred, namely in more than 83 per cent of the analysed cases. As to the variable DET, in an 1AM I would propose that the choice of determiner is not causally, but only indirectly, related to the choice of construction. Rather, as has been proposed throughout the functional literature, the choice of the determiner is, on the whole, contingent on the degree of giveimess of the referent of the ensuing noun (along the lines of my argument in section 4.3). Thus, the degree of activation of the noun is responsible for the choice of the determiner and the choice of construction; the statistical correlation between DET and particle placement does not result from any causal influence of DET. Finally, the variable TYPE can also be related to the notion of activation along the line of reasoning in section 4.3. Pronouns arc exclusively used for given referents. It has already been argued that, therefore, their nodes have been frequently and/or recently activated in the discourse preceding the VPCs. Thus, their activation levels are already fairly high or just in the hyperexcitability phase where repeated activation is expected so they are

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very likely to be selected at the point of time where the VPC is being proeessed. If the pronominal direct object can then be activated easily and fast, it is more likely to be selected when the system inspects the activation levels, so construction, has a greater chance of being used; note that the activation of pronominal referents seems to be even higher than the activation that particles of idiomatic VPCs receive from their unit node since even with the most idiomatic constructions, construction, is obligatory with pronominal direct objects: cf. (87). (87) a. *He eked out it. b. He eked it out.

Lexical nouns, by contrast, prefer to be used with brand-new or unused referents (whose activation levels are low) so, when it comes to selecting the construction, the activation of their nodes is thus too low, rendering these nodes unlikely to be selected early.

8.2.4 Phonological variables The only phonological variable that was theoretically included, though not empirically analysed, is the stress pattern of the verb phrase. At first glance, it seems quite difficult to relate the (iridings observed in the literature to the IAM elaborated above: since it is commonly argued that stressed items are more activated than unstressed ones (cf. Mac Kay 1971; Berg 1998: 107), one would expect stressed items to be produced early because insertion rules should select these highly activated items first. Previous works show, however, that stressed items (more precisely, contrastively stressed expressions) prefer sentence-final position in general. How do we reconcile this conflict? Possible answers to this question are along the following lines. It was previously argued that, in the lirst stages of generating an utterance, there is a non-linguistic representat ion of the information to be communicated. However, the information to be communicated does not only comprise the purely referential content, but also the communicative intention. For instance, different communicative intentions (such as, e.g., different speech acts) require the activation of different syntactic rules in order to achieve the desired communicative effect. In other words, the activation of syntactic productions is not only contingent on the particular semantic information to be communicated, but also on the pragmatics of the utterance. If, therefore, the speaker intends to strongly highlight the referent of the direct object or the denotatum of the particle, then the pragmatics of this utterance also differs from the standard utterance where no such emphasis/contrast is to be communicated. Therefore, just like the pragmatic; intention of asking a question activates the syntactic nodes representing questions, the pragmatic intention of contrastively highlighting some referent/denotatum also triggers associated syntactic nodes. Since end-focus is a very general characteristic of the English language, one may

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expect that the two syntactic nodes representing construction,, and construction, are more likely to be activated when the referent of the direct object and the denotatum of the particle respectively are to be contraslively stressed. However, one should also bear in mind that cases with coiitrastively stressed pronouns in construction,, are extremely rare. 8.2.5 Remaining variables On the basis of the Processing Hypothesis it was previously argued that PP has no causal influence on the choice of construction. This also holds, 1 submit, for the role of PP within lAMs. Given that the directional adverbial follows the VPC, I believe that no causal relation between activation and PP can be assumed in order to explain the correlation between particle placement and PP arid that the explanation for this correlation is the one proposed in Chapter 6, n. 18 namely the correlation between particle placement and IDIOMATICITY, a variable whose relation to activation is quite apparent. The variable DISFLUENOY can be naturally translated into a theory of activation. According to the above description of activation models, a case of disflucncy can be characterized as a state where the system is not able to select any one entity for production. In actual speech, then, such failures ol the system often manifest themselves as hesitations or filled pauses (e.g. er or um) serving to fill the silence and hold the floor until the next word is produced. Thus, although the present data show no strong correlation, findings of that ilk (e.g. the results of Arnold and Wasow 1996) can in principle be integrated into such an account without any problems. The translation of PART = PREP into activation models is similarly straightforward. It was previously mentioned that the selection of a node A is followed by a sudden decrease of A's activation level to zero (the so-callecl refractory phase; cf. above). While the activation level may rebound quickly, during this refractory phase A is highly unlikely to be selected again because its activation level is so low. Thus, once the particle has been selected for production, its activation level and, thereby, its likelihood of immediately repeated selection decreases. That is, if evidence for the relevance of PART = PREP had been found in the data it could be easily accommodated by an interactive theory of activation.2"' Finally, let us turn to a variable that has not been investigated in the main course of the analysis, since it has never been connected to particle placement, namely structural pruning. In the above characterization of lAMs, it was argued that there exists a level/network of nodes of syntactic productions (in the form of syntactic frames with slots) some of which could be activated by a matching semantic node even though this might constitute a violation of Occam's razor (cf. n. 9). Let us assume some speaker has produced a VPC in the form of construction,. After the selection of construction, the activation level of the corresponding node first decreases rapidly and then rebounds due to spreading activation from the less stronglyactivated neighbouring constructional nodes on the same level in the system.

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Consequently, after the short refractory phase, the constructional node's activation is well above resting level again (hyperexcitability phase) and thus a likely candidate for repeated selection once a semantic representation is activated that is compatible with the semantics of the VPC. If, therefore, the nodes of construction,, and construction, are activated again from the higher semantic level in the not too distant subsequent discourse (since they match the semantics of the utterance to be produced), then construction, has a higher chance of being activated as its activation is still above the base level. My findings concerning structural priming reported in section 6.4, thus, come as no surprise, once an IAM is assumed, but are difficult to explain in other theories based on processing (e.g. Hawkins 1994). However, structural priming has also more general ramifications for lAMs. Note that the influence of structural priming on particle placement cannot be explained unless we assume that there is a level/network of syntactic productions: if the choice of construction was only due to the selection of a lexical node (either the particle or the first word of the direct object), then there would be no way to account for the preference of repeating a construction within an IAM. This finding, thus, constitutes additional evidence for this level. 8.3 A network of variables and weighted (causal) relations The last sections have shown how all the variables analysed so far can be interpreted within an IAM. Some of the variables' statistical correlations with particle placement were argued to result from these variables' causal influence on particle placement; other statistical correlations of variables with particle placement were explained by arguing that these variables do not causally influence the alternation but are in turn causally correlated with those that do. Finally, one variable (structural priming) was discussed that most other theories would find hard to integrate. I will now point out several other characteristics of the present analysis that fit nicely with an activation-based approach and are also more problematic for other accounts. First, it was previously argued that the selection of syntactic structure depends, among other things, on the association strengths of the links between semantic, syntactic and lexical nodes. The multifactorial analysis used in the present study allows for a simple way to translate the weights Wx of links between nodes into our empirical findings: we can simply interpret the factor loadings of the discriminant analysis (or the predictor strengths of the CART analysis, for that matter) in terms of (i) the strengths of vertical function-form associations as postulated in the Competition Model (cf. Bates and MacWhirmcy 1989: 49), or (ii) even as connection weights. For instance, Table 6:34 reported that for pronominal direct objects a factor loading of .499 was obtained, leading to a preference for construction,. For an IAM this would mean TYPE: pronoun excites the node for construction, (or, more precisely, the node for the NP in the syntactic VP-frame of the VPC) with a strength of .499; this strongly increases the

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likelihood of this NP node being selected first, thereby increasing the probability of construction,. Second, it was previously explained that nodes (the two construeuonal nodes as well as all other nodes) have baseline activation levels that determine the ease with which a node can be activated. Our earlier multifactorial analysis of particle placement can easily accommodate this property of nodes since both discriminant and CART analyses require the user to specify a prior probability of the outcome of the dependent variable. For our phenomenon, this means the user needs to provide the general probabilities of construction,, and construction,. In the analyses reported in section 6.3.1 set both prior probabilities to .5; i.e. I assumed that, on the whole, both constructions are equally likely to occur. Suppose, on the other hand, a corpus analysis would have shown that the two constructions are distributed such that construction,, is nine times as frequent as construction,. For a discriminant or a CART analysis, we would therefore set the prior probabilities of construction,, and construction, to .9 and . 1 respectively in order for the analysis to reflect reality. Suppose further that a construction is generally selected once its activation level exceeds a strength of 1.0. In such a situation, the node for construction,, requires little extra activation (namely . 1) whereas the node for construction, requires a lot more extra activation (namely .9). Given these prior probabilities, the factor loadings computed by the analysis weigh the variables' contribution in such a way as to maximally correspond to the utterances by native speakers. In sum, the notion of base-line activation is, so to speak, inbuilt into the multifactorial procedures used previously. Finally, let us briefly consider the error rate of my prediction of the constructions. We have seen that the multifactorial analysis resulted in a comparatively high cross-validated success rate of 83.9 per cent. In the processing account suggested previously, I demonstrated that the wrongly predicted cases are those that are characterized by variables' values/levels that do not possess a strong preference for one construction so that the no processing advantage is immediately obvious to the speaker and no immediately obvious choice of construction follows. Of course, the question arises as to how this error rate can be explained in an I AM. As was previously mentioned, part of the activation flow in the system at time step t is a certain degree of background noise within the system. At the time when the system inspects the activation levels of both constructional nodes, the background noise can influence the choice of construction in cases where the two constructional nodes have similar activation levels. Suppose again we have two constructional nodes C0 and C, for construction,, and construction, respectively. These nodes have, at time t where the system inspects the activation levels in order to activate a construction, activation levels of A (C(), t) and A (C,. t) respectively. When A (C,. t) is larger than A (C(l. t) (i.e. when A (C,, t) — A (C,,, t)>0), then construction, is activated; by contrast, when A (C,,. t) is larger than A (C b t) (i.e. when A (C,, I.) — A (C,,, t)l), then noise does have little influence on the activation levels and, therefore, out of 211 such cases only six errors occur. This does not constitute irrefutable evidence of the concept and influence of noise in the system, but it is yet another piece of evidence that (i) fits nicely into the analyses in terms of IAMs, and (ii) is difficult to account for, or less elegant to incorporate, in competing theories. In view of the complexity of the system of interrelated variables advocated in the previous sections of this chapter and the representations of activation flow between nodes, it would also be helpful to visualize this network of variables. Figure 8:7 shows how the variables and their relations to activation can be characterized in the form of a single diagram, representing the interconnections of variables on the basis of the arguments adduced in Chapters 2, 4, 6 and 8. Arrows symbolize causal relationships (from a cause to an effect), broken lines symbolize correlational, but not causal, relationships. The figures represent the strength of the vertical function-form relation of the Competition Model (at least partly intcrpretable as weights of links between nodes) as measured by standard coefficients of correlation on the basis of my data. The horizontal axis on the bottom represents a timeline along which the above activation patterns occur.24 Since the present study pursued two goals, a linguistic one and a methodological one, this diagram represents, in a way, the summary of my pursuit of the linguistic objective, namely to provide a truly multifactorial description and explanation of particle placement.

Figure 8:7 A network of variables with intercorrelations/association strengths (cross-validated prediction accuracy of the two constructions: 83.9 percent)

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Given this representation, it would be desirable to decide whether this model is appropriate for particle placement in English, given our data. While we have seen that in general it is virtually impossible to decide on a particular cause-effect model on the basis of correlations alone, statisticians have developed techniques that are used for testing how causal models conceived by the researcher fit the structure of the data investigated. These models come under different headings, although their overall line of reasoning is quite similar: path analysis, structural equation modelling or LISREL (for Linear Structural Relationships). In view of the amount and the complexity of the calculations involved to test the above path diagram, causal modelling is, unfortunately, a statistical application for which a larger data set needs to be available. However, in order to provide an idea of what is possible once statistical techniques of such sophistication are used more often, let us briefly test one small subpart of the above diagram, namely the one that is needed to discuss/refute Hawkins's claim of the irrelevance of discourse-functional variables. In section 7.3, I argued that, on the basis of the correlations Hawkins (1994: section 4) presents, several cause-effect relationships are conceivable, which were then illustrated by Figures 7:3 through 7:5. In order to determine which of these diagrams is most appropriate for my data, I used the Structural Equation Modelling (SEPATH) module of Statistica 99, which is an extension of J0reskog's L1SREE. Eor Hawkins's EIC, 1 used the variables COMPLEX, LENGTHW and LENGTHS for the discourse-functional variables, I used AcxPG, TOSM and ConPC; the results are given in Table 8:1. These results are to be interpreted as follows: GFI and AGFI are historically the most widely known indices for structural equation modelling. They are goodness-of-fit indices representing how well the model fits the data: the value represents the percentage of variance of particle placement that can be accounted for by the model. The AGFI index is the more relevant of the two since it is adjusted for the complexity of the model and, thus, much more reliable. We see that the model argued for by Hawkins does not do well compared to the other two, which do not treat discourse-functional variables as merely epiphenomenal: the latter two models account for more variance of particle placement in the sample investigated. The two more modern Gamma indices (of which, again, the adjusted index deserves most attention) support this analysis by showing that we need not restrict this claim to Table 8:1 Structural equation modelling results Model

GFI

AGFI

Population Gamma Index

Adjusted Population Gamma Index

RMSR

Figure 7.3 Figure 7.4 Figure 7.5

.902 .917 .948

.805 .821 .879

.911 .925 .956

.821 .837 .897

21.1% 11.7% 6.3%

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the sample investigated but can also extend it to the larger population: again the latter two models (where discourse-functional variables are assumed to have a [direct and/or indirect] causal influence on particle placement) better fit the data than the model derived from Hawkins's claims where only EIC is causally relevant. This is especially obvious from the rightmost column in Table 8:1: the RMSR value states how much variance in the data the respective model cannot account for, and we see that the model based on Hawkins's claims leaves about three times more variance unaccounted for than the subpart of my model in Figure 8:7. In a way; this is not really surprising: if, as my data suggest, the relation between the variety of variables postulated in my analysis is as represented in Figure 8:7, then an analysis singling out a few of these variables and claiming that one of them is solely responsible is bound to fare worse than analyses having a somewhat wider scope. Is there any evidence supporting the sub-part model of Figure 7:5 (here repeated for case of reference as Figure 8:8) other than the results of the structural equation modelling analysis? Yes, there is. First, it is obvious that the correlations we find between discourse-functional variables concerning the preceding context and the morphosyntactic variables only support the causal relationship represented in Figure 8:8 because the converse cause-eilect relation would have to work backwards in time. That is to say, given the temporal unidirectionality of discourse, the morphosyntactic complexity of constituents C, and C 2 in some utterance U cannot influence the discourse status of C, and C,, before U was even produced. That is, morphosyntax cannot be the sole cause for everything; something must be located temporally/causally before it, even if this something is then in turn constrained by morphosyntax. Second, in some respects one would even intuitively expect such a result. If the morphosyntactic variables are not necessarily influenced by anything else (which would follow from Hawkins's claim that all variables other than his complexity variables are purely epiphenomenal), then how would he explain that speakers sometimes use pronouns for direct object referents in VPCs and sometimes not in the first place? Well, Hawkins cannot explain this since all variables he allows for arc morphosyntactic. With a cognitiveiunctional perspective, however, the answer is quite obvious: speakers'

discourse-functional variables^v.

morphosyntactic variables particle slacement

Figure 8:8 A subpart of the proposed causal activation network

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MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS

choices of, say, pronouns are determined by discourse-functional factors. Thus, the present approach can explain what lies behind Hawkins's variables since it is not constrained by viewing everything apart from morphosyntax as epiphenomenal. Of course, apart from the correlation between morphosyntax and particle placement (arrow @), Hawkins is also aware of the correlation between length and, say, givenness (arrow ®). The point to be made is that he reduces the correlation to the question 'which is the chicken and which is the egg?', without acknowledging that a simple monodirectional causal explanation is not the only one let alone the most plausible one that is licensed by his observations. Finally, the discourse-functional variables cannot be purely epiphenomenal since they improve our ability to predict the choice of construction directly (arrow ®). Let us look at just a single and extraordinarily simple example supporting this claim. The corpus data show that short direct objects (i.e. object NPs with less than four syllables) prefer construction,: 168 (73.36 per cent) out of 229 VPCs with short direct objects occurred in coristruction,. Similarly, the corpus data also show that previously mentioned referents of direct objects prefer construction,: 143 (72.96 per cent) out of 196 VPCs with previously mentioned referents of direct objects occurred in construction,. Apparently, both variables' levels predict the constructional choice equally well, contrary to what has been claimed by Hawkins. If, however, both levels are combined (i.e. we look at the distribution of constructions for short objects and given referents), then the distribution is even more extreme: 126 (85.14 per cent) out of 148 VPCs are construction,. That is to say, from Hawkins's perspective, if LENGTHS is supplemented by LM, the prediction accuracy is improved by about 12 per cent, something that can hardly be explained by assuming that LM is purely epiphenomenal. Rather, we infer that givenness and particle placement are also causally related, and I have proposed that the relation is due to the degree of activation of the direct object (cf. section 8.2.1). In sum, the analysis of the model in Figure 7:5/Figure 8:8. making up only a small subpart of my overall causal activation model in Figure 8:7, lends support to Hawkins's results that the morphosyntactic variables are quite powerful. However, it goes beyond it by illustrating that Hawkins's conclusions (about discourse-functional variables being merely epiphenomenal) are fundamentally mistaken. More importantly, however, the analysis of the smaller model in Figure 7:5/Figure 8:8 also lends some credibility to the larger network of causal activation connections of which it is a part. Therefore, although a methodologically optimal analysis on the basis of structural equation modelling awaits a larger data set. we have obtained primaj'acie evidence for the causal network proposed above. 8.4 Interim summary

We have seen that virtually all variables' effects can be explained in an interactive activation model of sentence production as proposed at the

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beginning of this chapter. That is, lAMs can be fruitfully applied to the analysis of syntactic variation. It is important to note, however, that, while the Processing Hypothesis and the IAM do make virtually identical predictions, this is not meant to imply that there is by necessity an isomorphic relation between the two approaches; on the other hand, when we looked at the variables COMPLEX as well as LENGTHS and LENGTH\V. the relation between the two approaches was quite apparent. In a way, from the perspective of lAMs, processing is nothing but activation flow within the system, and what has traditionally been looked at in terms of processing effort might as well be more fruitfully described as the summation of activation in nodes, where amount of processing effort does not translate into, but is compatible with, the amount of nodes held active within a particular time interval. I consider it to be one of the most important findings of the present analysis that phenomena whose analyses have hitherto relied on traditional accounts in terms of processing effort can be more directly and, thus, rcwardingly explained on the basis of lAMs. From the present perspective, two other important advantages of the activation-based approach are also evident once we return to the two questions initially raised in this chapter. It is now possible to predict speakers' choices of construction by clearly relating all variables to the mental processes taking place in only one of the interlocutors, namely the speaker; at the same time, the activation account also relieves us of requiring the speaker to (i) compute the processing effort associated with the two constructions, and (ii) select a construction on the basis of the results of his computation. By going beyond simpler activation models such as the one underlying my discussion of variables in section 4.1 (based on Givon 1992b and Lambrccht 1994), it is now possible to predict speakers' choices without the two slightly questionable assumptions implied by accounts in terms of processing cost, while at the same time integrating findings that processing accounts have difficulties in explaining (e.g. structural priming). Notes 1 This chapter has benefited greatly from many stimulating discussions with Thomas Berg (University of Hamburg), who of course might not agree with all of what follows. '2 This presentation is based on Bates and MacWhinney (1989), whose discussion has focused on comprehension, and Bates and Dcvcscovi (1989), who are concerned with production. 3 Cf. MacWhinncy (1989) on the relation between connectionism and the Competition Model. 4 The selection of these two authors is not meant to imply that their models are completely identical, but an exhaustive discussion of the different kinds of lAMs and the data motivating the different proposals are well beyond the scope of the present work as is a comprehensive justification for the nature of the model that I will adopt. 5 Some authors have postulated activation thresholds which have to be exceeded

182

6

7

8 9

10

11

MULTIFACTORIAL ANALYSIS IN CORPUS LINGUISTICS by incoming activation or priming before a node can fire and thereby pass on activation to its neighbouring nodes (cf. e.g. MacKay 1987); the height of such a threshold of some node is determined by the frequency with which this node has been activated previously. However, I follow Stemberger (1985: 147) and Dell (1986: 287) in omitting activation thresholds, saturation points and other non-linear influences. At this point, several models make different claims. On the one hand, McClelland and Rumelhart (1982: 379) arid Stemberger (1985) argue that, apart from excitatory activation, there is also inhibitory activation. More precisely, McClelland and Rumelhart claim that all neighbouring nodes within the same level receive inhibitory activation whereas all other neighbouring nodes (from adjacent levels) receive excitatory activation. On the other hand, MacKay (1987) claims that nodes do not pass on activation, but priming. In my discussion, I follow Dell (1986: 288) and assume excitatory activation only. 'Self-inhibition is the inhibitory process that terminates the self-sustained activation of [. . .] nodes and temporarily reduces their priming level to below normal or resting state' (MacKay 1987: 141). According to MacKay (1987: 141), the duration of this refractory phase is only 1ms. Cf. Dell (1986: 287-8) for a conceptually similar though slightly different formula. This might raise the question whether a syntactic level (of constructional nodes) is required in the system. As evidence for such a level, for instance, Stemberger (1982a) argues that a speech error such as roll up it (for roll it up) might have occurred due to an overly high activation level of a syntactic rule that places particles right after their verbs. However, it is easy to see that this is not a persuasive argument: if no level of constructional nodes existed, it would be possible to ascribe the error to noise on the word level such that, at the point of word selection, the activation level of up was higher than that of it. Thus, in order not to violate Occam's razor, more conclusive evidence is required to support this kind of nodes. A first intuitive reason to do so is to constrain the power of activation models: were it not for some syntactic constraints that can be captured in the form of constructional nodes, activation models would regularly violate language-specific word order patterns by selecting highly activated words without consideration of syntactic structures into which these words need to be embedded. However, I will take up this issue again later and will provide some empirical evidence on the basis of the present data. Obviously, a textual (and thus linear) representation of a multitude of reputed parallel processes nearly amounts to a contradiction in terms; expressions like and then, etc. are therefore to be taken with a pinch of salt. Also, I will not be able to provide a comprehensive discussion of the many potentially relevant issues such as. e.g., the time course of lexical access (cf. Dell and O'Seaghdha 1992), the degree of locality of interactions between semantic and phonological information (cf. Bock 1990; Dell and O'Seaghdha 1992) or whether production is strictly incremental or competitive (cf. Fcrrcira 1996). In the following discussion I do not commit to cither a local representation (i.e. a representation where there is one node representing John) or a distributed representation of concepts (i.e. where the concept John is represented by a distributed pattern of activation over many nodes) since this distinction does not bear on the issue. Moreover, I will leave aside details concerning past tense marking of the verb as well as lemma selection and phonological encoding (cf. Dell et al. 1999).

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12 The criteria used to determine the fit between the semantic representation and alternative syntactic phrase structures arc concerned with 'the structural aspect of semantic information [. . .] e.g., the grammatical relations of the noun phrases, modulations such as aspect, definiteness, etc.' (Stemberger 1982b: 320). 13 The representation of levels in terms of a hierarchy is just chosen for convenience. 14 I assume that several syntactic structures that are structurally mutually incompatible can be constructed simultaneously. There is evidence for this assumption from the analysis of syntactic blends (cf. Fay 1980 and Stemberger 1985). 15 Alternatively, we might assume that the weight of the links from S to NP are stronger, thereby facilitating early selection, but both descriptions fit our purposes. Also, the NP node may already have received some activation spreading from the 'nouny' node John,. 16 Alternatively, it is argued that a set of so-called insertion rules is operative (cf. Dell 1986)." 17 1 follow Dell's (1986: 288) definition of current node: 'It is that item of the higher level representation that is in the process of being translated into corresponding items at the immediately lower level', which entails that, at a given point of time, there can be several current nodes, namely on different levels of the system. 18 This summary cannot do justice to all the details of such networks. While the above characterization is somewhat more detailed than many previous descriptions, I will comment on many other minor details in the sections to follow, but cL e.g., McClelland and Rumelharl (1981: 377 85), Stemberger (1985) and Dell (1986: 287 9) for a different version of an IAM, the so-called node structure theory cf. MacKay( 1987). Note also that experimental work on English TPVs is still necessary to support the above characterization. To my knowledge, contemporary psycholinguistic studies on the representation and processing of morphologically complex verbs have focused largely on Dutch and German verbs (both separable vs. inseparable \aujsagen vs. unlersuchm] and literal vs. idiomatic \iveggeben vs. aufgeben\). Thus, unfortunately, there seem to be only a few studies where such issues were investigated experimentally for English complex verbs (cf. studies by Hillcrt and his associates). Besides, most of the studies on particle verbs (i) address matters of comprehension rather than production, and (ii) do not relate their findings to lAJVls of the sort discussed here. 19 While section 4.1 has already related the discourse-functional variables to activation, note that the present treatment of these variables within lAMs differs from the previous one. The explanation in section 4.1 was based on a fairly nontechnical definition of activation and was concerned with the benefits that the given-new organization provided by the speaker has for the hearer; note also that it faces the two problems of processing accounts mentioned previously. The analysis in terms of an IAM exclusively focuses on the speaker's production processes without requiring such a conscious, cooperatively planned organization of the discourse, thereby making some of the claims in section 4.1 more explicit and avoiding these potential pitfalls. 20 Note that this effect cannot be reduced to frequency; cf. Chapter 2, n. 26. 21 In my data, the average ConPC value of pronominal and non-pronominal object nouns is 6.9 and 2.6 respectively; this difference is highly significant: tvv,,,h( 100) =-8.98: p-i

low

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APPENDICES

10.2 Register-dependent interaction plots

Figure 10:1 Interaction plot: construction X REGISTER X COMPLEX

Figure 10:2 Interaction plot: construction X REGISTER X LENGTH\¥

APPENDICES

Figure 10:3 Interaction plot: construction x REGISTER X LENGTHS

Figure 10:4 Interaction plot: construction X REGISTER X TYPE

195

196

APPENDICES

Figure 10:5 Interaction plot: construction X REGISTER X DET

Figure 10:6 Interaction plot: construction x REGISTER X IDIOMATIGITY

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Figure 10:7 Interaction plot: construction X REGISTER X CONCRETE

Figure 10:8 Interaction plot: construction X REGISTER X ANIMACY

197

198

APPENDICES

Figure 10:9 Interaction plot: construction X REGISTER x LM

Figure 10:10 Interaction plot: construction X REGISTER X AcxPG

APPENDICES

Figure 10:11 Interaction plot: construction X REGISTER X TOPM

Figure 10:12 Interaction plot: construction x REGISTER X CoHPC

199

200

APPENDICES

Figure 10:13 Interaction plot: construction X REGISTER X NM

Figure 10:14 Interaction plot: construction X REGISTER X CursSC

APPENDICES

Figure 10:15 Interaction plot: construction X REGISTER X TOSM

Figure 10:16 Interaction plot: construction X REGISTER X ConSC

201

202

APPENDICES

Figure 10:17 Interaction plot: construction X REGISTER X OM

Figure 10:18 Interaction plot: construction X REGISTER X PP

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203

10.3 ListofTPVs1 ace out act out add in add on add up answer back ante up argue down argue out ask in ask out auction off average out back up bag up bail out bail up balance out balance up bale out ball up bandage up bandy about bandy around bang down bang out bang up bark out base on base upon bash in bash out bash up bat around bat out batten down batter down bawl out beam down beam up bear away bear off bear out bear up

beat back beat down beat out beat up bed out beef up bellow out belt out belt up bid up bind over bitch up bite back black out blank off blank out blast out block in block off block out block up blot out blow away blow down blow off blow out blow over blow up bluff out blurt out board up bog down boil away boil down boil off boil out boil up bollix up bolt down boot out boss about boss around botch up bottle up

bounce around bowl out bowl over box in box up brave out brazen out break down break in break off break out break up breathe in breathe out brew up brick up brighten up bring about bring along bring around bring back bring down bring forward bring in bring off bring on bring out bring round bring together bring up broaden out brush aside brush away brush down brush off brush out brush up buck up bugger about bugger up build in build up bulk out bum out

bump off bump up bunch up bundle off bundle up bung up buoy up burn down burn off burn out burn up bust up butter up buttress up buy back buy in buy off buy out buy up call away call back call down call forth call in call off call out call over call up calm down cancel out carry away carry forward carry off carry on carry out carry over carry through cart off carve out carve up cash in cast aside cast down cast off

204

cast out cast up catch out catch up cave in cement up chain down chain up chalk up change around change round charge up chase away chase down chase off chase up chat up check off check out check over cheer on cheer up chew out chew over chew up chip in chivvy along chivvy up choke back choke down choke off choke up chop down chop up chuck away chuck in chuck out chuck up churn out churn up clap out claw back clean down clean out clean up clear away

APPENDICES

clear out clear up clock up clog up close down close off close out close up clue in clue up cobble together cock up collect up colour in colour up comb out cone off conjure up connect up consign over contract out cook up cool down cool off coop up copy down copy out cordon off cough up count down count in count off count out count up cover over cover up crack off crack up crank out cream off crease up cross off cross out cross up crowd out crumple up

cry out cut back cut down cut in cut off cut out cut up dam up damp down dash off deal out deck out deliver over deliver up dig in dig out dig over dig up dish out dish up divide off divide up divvy out divvy up do down do in do off do out do over do up dob in dole out doll up dope out dope up drag down drag in drag out drag up draw down draw in draw off draw out draw up dream up dredge up

dress down dress up drink in drink up drive away drive back drive off drive out drop in drop off drum out dry off dry up duff up dumb down dust down dust off ease oil' ease out eat away eat off eat out eat up edge out edit out egg on eke out elbow out empty out even out even up explain away eye up face down factor in factor out fade in fade out fake out fan out fancy up farm out fasten up fathom out fatten up feed up

APPENDICES

feel out feel up fence in fence off fend off ferret out fetch up fiddle away fight back fight down fight off fight out figure out figure up file awav fill in fill out fill up filter out find out finish off finish up fire off fire up firm up fish out fit out fit up fix up flag down flash around flash out flatten out flesh out fling oil' flip'off flood out fluff out fluff up flush out fly in fob off fog up fold away fold in fold up

follow out follow through follow up force back force out fork out fork over fork up foul up freak out free up freeze out freshen up frig up frighten away frighten off fritter away frizzle up frown down fry up fuck over fuck up gas up gather in gather up gear up get across get back get down get in get off get out get over get together get up ginger up give away give back give in give olf give out give up glass in gloss over goad on gobble down

gobble up goof up gouge out grass on grass over grass up grind away grind down grind out grind up gross out grub out grub up gulp down gum up gun down gussy up hack off hammer in hammer out hand around hand back hand down hand in hand on hand out hand over hand round hang out hang up hash out hash over hash up haul in haul olf haul up have in have off have on hawk about hawk around hawk round head off head up heap up hear out

205

heat up heave up hedge about hedge around hedge in hedge round help out hem in hide away hike up hire out hiss down hit back hit up hitch up hive off hoard away hoke up hold back hold down hold off hold out hold over hold up hollow out honk up hook up hose down hound out howl down hunt down hunt out hunt up hurry up hush up hype up ice down idle away ink in ink out invalid out invite along invite around invite back invite out invite over

206

iron out jack in jack up jazz up jerk around join up jolly along jot down juice up jumble up keep away keep back keep down keep in keep off keep on keep out keep up key in kick about kick around kick back kick down kick in kick off kick out kick over kick up kill off kiss off kit out knit together knock about knock around knock back knock down knock off knock out knock over knock together knock up lace up ladle out lap up lash down laugh down

APPENDICES

laugh off lay aside lay away lay by lay down lay in layoff lay on lay out lay up lead off lead on leave aside leave back leave behind leave down leave off leave out lend out let down let in let off let out level off level out lick off lift up light up lighten up line up link up liquor up live down live out liven up load down load up loan out lock away lock in lock out lock up look out look over look up loosen off

loosen up lop off louse up lug back lump together magic away mail out make off make out make over make up map out mark down mark off mark out mark up marry off marry up match up max out measure off measure out measure up mellow out melt down mess over mess up mete out miss out mix up mock up mop up move down move in move on move out move up mow down muck out muck up muddle up mug up mull over muscle out muss up muster in

muster out muster up nail down nail up narrow down nose out notch up note down nut out offer up open out open up order in pace out pack away pack down pack in pack off pack out pack up pad out palm off parcel out parcel up pare down partial out partition off partner off partner up pass around pass away pass down pass off pass on pass out pass over pass round pass up patch together patch up pay back pay down pay in payoff pay out pay up

207

APPENDICES

peel off peg down peg out pen in pen up pencil in pension off pep up perk up phase in phase out phone up pick off pick out pick over pick up piece together pile on pile up pin back pin down pin up piss away piss off plan out plant out play back play down play out play up plonk down plop down plot out plough back plough up plow back plow up plug up plumb in plump up plunk down point out point up polish off pony up portion out

post off pot up pour out power up prick out prick up print off print out prop up psych out psych up puff out puff up puke up pull apart pull away pull back pull down pull in pull off pull out pull over pull together pull up pump in pump out pump up punch in punch out punch up push aside push away push back push off push out push over push through push up put about put across put around put aside put away put back put down put forth

put forward put in put off put on put out put over put round put together put up puzzle out quiet down rack up raffle off rain down rake in rake off rake up rap out ratchet up ration out rattle off reach down reach out read back read off read out read over read through read up reason out reckon in reckon up reel in reel off reel out rein back rein in render down rent out report back report out rev up ride out rig out rig up ring back

ring down ring out ring up rinse out rip apart rip off rip up roll back roll out roll over roll up root out root up rope in rope off rough in rough out rough up round off round out round up rout out rub down rub in rub off rub out rub up ruck up ruffle up rule off rule out run down run in run off run out run over run up rush out rush through rustle up saddle up salt away sand down save up scale back scale down

208

scale up scare away scare off scare up scarf down scarf up scoop out scoop up scope out score off score out scout out scout up scrape together scream out screen in screen off screen out screw down screw over screw up scribble down scrounge up scrub out scrunch up seal in seal off seal up search out section off see off see out seek out sell off sell on sell out sell up send away send back send down send in send off send on send out send up separate off

APPENDICES

separate out serve out serve up set apart set aside set away set back set down set off set out set up sew down sew up shade in shake down shake off shake out shake up share out sharpen up shell out shin up ship off ship out shoot down shoot off shoot up shore up shout down shout out show in show off show out show up shrug off shuck off shut awayshut down shut off shut out shut up sick up sift out sign away sign in sign off

sign on sign out sign over sign up silt up sing out single out siphon away siphon off sit down sit out size up sketch in sketch out slag off slam down slap down slap on sleep off slice off slice up slick up slim down slip in slip off slip on slip out slot in slough off slow down slow up sluice down sluice out smarten up smash down smash in smash up smell out smoke out smooth away smooth down smooth out smooth over snap up snarl up sniff out

snuff out soak up sober up sock away sock in soften up sort out sound out soup up space out spark off speed up spell out spew up spice up spiel off spiff up spill out spin off spin out spit out spit up split off split up sponge down spoon out spread out spruce up spur on square away square off squash in squeeze in squeeze out squinch up squirrel away stack up stake out stall off stammer out stamp out stand up stare down stare out start off start up

209

APPENDICES

starve out stash away stave off steam up step down step up stick down stick out stick up stiffen up stink out stink up stir in stir up stitch up stoke up stop up store away store up stow away straighten out straighten up strap in strap up stretch out strike back strike down strike out strike up string along string out string together string up strip away strip down strip off stub out stuff up stump up suck in suck off suck up sum up summarize up summon up suss out

swallow up swear in sweat out sweep aside sweep away sweep out sweep up swill down switch off switch on swot up tack on tag on take along take apart take around take aside take away take back lake down take in take off take on take out take up talk down talk out talk over talk through talk up tamp down tangle up tap out tape up tart up tear apart tear away tear down tear off tear up tease out tee off tee up telegraph in tell apart tell off

lest out thaw out thin down thin out think out think over think through think up thrash out throttle back throttle down throw away throw back throw down throw in throw off' throw on throw out throw over throw together throw up thunder out tick off tidy away tidy out tidy up tie back tic down tie up tighten up tip off tip over tip up tire out tog out tone down tone up tool up top off top up toss about toss around toss back toss down toss off lot up

total up tote up touch up tough out toughen up tout around tout round trace out track down trade in trade off train up tread down trigger off trim away trim off trip up trot out true up truss up try on try out tuck away tuck in tuck up tucker out tune down tune off tune out tune up turf out turn around turn away turn back turn down turn in turn off turn on turn out turn over turn round turn up type out type up urge on use up

APPENDICES

210

vamp up vote down vote in vote out wait out wake up walk off walk up wall in wall off wall up ward off warm over warm up warn away warn off wash away wash down wash off

wash out wash up water down wave aside wave down wave off wave on wear away wear down wear in wear off wear out weed out weigh out weigh up wheel out while away whip out whip up

whittle away whittle down win around win over win round wind down wind on wind up winkle out wipe down wipe off wipe out wipe up wire up wish away wolf down work in work off work out

work over work through work up wrap up wring out write back write down write in write off write out write up x out yank off yank out yank up yell out yield up zip up

Note 1 This alphabetical list is only formally/structurally organized so that TPVs with more than one meaning (such as throw up] arc not listed twice.

11

References

Aarts, B. (1989) Verb-preposition constructions and small clauses in English. Journal of Linguistics 25: 277-90. Aarts, B. (1992) Small Clauses in English: The Non-Verbal Types. Berlin, New York: Mouton de Gruytcr. Abney, S. (1996) Statistical methods and linguistics. In J. Klavans and P. Rcsnik (eds), The Balancing Act. Cambridge, MA: The MIT Press, pp. 1 26. Altenberg, B. (1982) The Genitive Construction v. the of-Construct! on: A Study of Syntactic Variation in Seventeenth Century English. Lund: CWK Leerup. Anderson, J. R. 1996. Rognitwe Psychologic, 2nd cd. Heidelberg, Berlin. Oxford: Spektrum. Armstrong, S. L., L. R. Gleitman and H. Glcitman (1983) What some concepts might not be. Cognition 13: 263-308. Arnold, J. E. and T Wasovv (1996) Production Constraints on Particle Movement and Dative Alternation. Poster presented at the CUNY Conference on Human Sentence Processing. Arnold, J. E. et al. (2000) Heaviness vs. newness: the effects of structural complexity and discourse status on constituent ordering, language 76: 28 55. Baker, M. (1988) Incorporation: A Theory of Grammatical Function Changing. Chicago: University of Chicago Press. Bates, li. and A. Devescovi (1989) Crosslinguistic studies of sentence production. In B. MacWhinney and E. Bates (eels), The Crosslinguistic Study of Sentence Processing.

Cambridge: Cambridge University Press, pp. 225-53. Bates, E. and B. MacWhinney (1982) Functionalist approaches to grammar. In E. Wanner and L. R. Gleitman (cds), Language Acquisition: The State of the Art. Cambridge: Cambridge University Press, pp. 173-218. Bales, E. and B. MacWhinney (1989) Eunctionalism and the competition model. In B. MacWhinney and E. Bates (eds), The Crosslmgms/ic Stud}1 of Sentence Processing. Cambridge: Cambridge University Press, pp. 3-73. Beals, K. et al. (eds) 1994. Papers from, the, Thirtieth Regional Meeting of the Chicago Linguistic Society, Vol. 2: The Parasession on Variation in Linguistic Theory. Chicago: Chicago Linguistic Society. Behaghel, O. (1930). Von dcutscher Wortstellung. ^eitschriftfur Deutschkunde ^'\: 81-9. Behaghel, O. (1932) Deutsche Syntax: Erne Geschichtliche Darstellung, Vol. IV: Wortstellung, Penodenbau. Heidelberg: Carl Winters. Berg, T. (1998) Linguistic Structure and Change: An Explanation from Language Processing. Oxford: Oxford University Press.

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Tyler, L. K. and W. D. Marslen-Wiison (1977) The on-line effects of semantic context and syntactic processing. Journal of Verbal Learning and Verbal Behaviour 16: 683-92. Urban, S. and A. D. Fricdcrici (1999a) Zur Bereitstellung von Vcrb-ArgumentStrukturen im Sprachverstehensprozep: Evidenzen aus ercigniskorrelierten Hirnpotential-Untersuchungen. In I. Wachsmuth and B.Jung (cds) Proceedings der 4. Fachtagung der Gesellschaftfur Kognitionswissenschajlen Biekfeld (28.9.-1.10.99). Sankt Augustin: Infix-Verlag. Urban, S. and A. D. Friedcrici (1999b) Zur Vcrarbeitung komplexcr Verben: Zwei EKP-Studien mil separicrbaren komplexen Vcrben. In E. Schrogcr, A. Mecklinger and A. Widmarm (eds) Experimentelle Psychologic. Beitrdge zur 41. Tagung experimentell arbeitender Psychologen, Leipzig (28.3, 1.4 1999). Letigerich: Pabst Science Publishers. Van Dongen, W A., Sr. (1919) He puts on his hat and He puts his hat on. .Neophilolo, ? zw4:322-53. Van Hout, R. (1995) Statistics. In J. Verschueren, J.-O. Ostman and J. Blommacrt (eds) Handbook of Pragmatics: Manual. Amsterdam, Philadephia: John Benjamins, pp. 624-7. Vennemann, T. (1988) Preference iMwsfor Syllable Structure and the Explanation of Sound Change: With Special Reference to German, Germanic, Italian, and Latin. Berlin, New York: Mouton de Gruyter. Vestcrgaard, T. (1974) Review of Bolinger (1971). English Studies 55: 303-8. Von Schon, C. V (1977) The Origin of Phrasal Verbs in English. Doctoral Dissertation. State University of New York at Stony Brook. Wardaugh, R. (1986) An Introduction to Sodolinguistics. Oxford: Basil Blackwell. Wasow, Th. (1997a) End-weight from the speaker's perspective. Journal of Psycholinguistic Research 26: 347-61. Wasow, Th. (1997b) Remarks on grammatical weight. Language Variation and Change 9: 81-105. Werner, J. (1997) Lineare Statistik: Das A llgemeine LineareModel!. Weinhcim: Psychologic Verlags Union. Western, A. (1906) Some remarks on the use of English adverbs. Englische Studien 6: 75-100. Winer, B. J., D. R. Brown and K. M. Michels (1991) Statistical Principles in Experimental Design. 3rd ed. New York: McGraw-Hill. Yeagle, R. (1983) 'The Syntax and Semantics of English VPCs with off: A Space Grammar Analysis'. Unpublished MA thesis, Southern Illinois University at Carbondale. Zofel, P. (1992) Statistik in der Praxis. 3rd rev. and extended edition. Stuttgart, Jena: Gustav Fischer.

Dictionaries /•works used for the list ofTPVs Benson, M., E. Benson and R. Ilson (1997) BBI Dictionary of English Word Combinations. Revised edition. Amsterdam, Philadelphia: John Benjamins. Cambridge International Dictionary of Phrasal Verbs (1997) Cambridge: Cambridge University Press. Collins Cobmld E-Dict CD-ROM (1999) London: Harper Collins. Courtney, R. (1983) Longman Dictionary of Phrasal Verbs. London: Longman. Cowie, A. P. and R. Mackin (1975) Oxford Dictionary of Current Idiomatic English. Volume 1: Verbs with Prepositions and Particles. Oxford: Oxford University Press.

REFERENCES

221

Cowic, A. P. and R. Mackin (1993) Oxford Dictionary of Phrasal Verbs. Oxford: Oxford University Press. Rudzka-Ostyn, B. (1999) English Phrasal Verbs: A Cognitive Approach. Manuscript.1 Note 1 I thank Paul Ostyn for generously providing this manuscript for my study.

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Subject index

acquisition 142, 154-5 n. 11, 190 activation discourse-functional 48-52. 56—7, 74, 89-95, 99 noise 160-1, 175-6 psycholinguistic 7, 9, 62 n.2, 130 n.34, 144-5, 158-81, 187-9 resting level 159-60, 166-7, 169, 174, 175 attention 5-6, 48, 63 n.6 CART 109, 115-18, 174 5, 186 categorization 5-6, 132-43, 186 Competition Model 7, 143, 157- 9, 174, 176

conflict validity 158 9, 186 construal 6, 24, 26 Construction Grammar 6, 53, 139 40, 154 n.10, 190 cue validity 133. 153n.3, 158 Dative Alternation 1, 47, 120, 151, 153 4n.5, 189 diachrony 3 5. 21, 142, 190 discriminant analysis 108 18, 134- 9, 174-5, 186, 189

EIC

37 n.7, 59 60, 65 n. 14, 146- 52, 178 180

locus end-focus 5. 24-5, 52 3. 55-6, 58, 59-60 end-weight, see end-focus semantic focus, see variables: semantic focus General Linear Model (GLM) 108-9, 113 given, see variables: ActPC/Dtlm, Lm, Topm identifiabilitv

49-51, 56-7

importance, w variables: ClusSC/Dtnrn, ' 1 bsm inhibition 160, 182 n.6-7 LISREL. see structural equation modelling metaphor

16, 63 n.8, 72, 77 n.9-10, 129

new; see variables: ActPC/Dtlm. Lm, Topm preposition vs. adverb 2. 17 Preposition Stranding 43 n.34, 189, 191 n.1 priming 119-20, 131 n.37, 154 n.6, 173-4, 181, 186 Processing Hypothesis 8, 48-52, 55. 59, 60-1, 62 n.1, 82, 85, 87-90, 92, 99, 111, 113-15, 120, 124-5, 130n.33, 131 n.38, 132, 185 7 prototypes/prototypicality 6, 8-9, 132-43, 152- 3 n.2 3, 186, 190 rhythmic alternation

40 n. 17

structural equation modelling 178 80, 187-9 syllable structure 120-1

157,

topicality, w variables: ActPC/Dtlm. Topm variable rules 143-6 variables all 23, 61-2, 98-101, 103, 106, 110-11, 114-17, 177 ActPC/Dtlm 19-20, 28-9, 39 n.13, 51, 57, 58, 65 n.13, 66 n.17, 72 4, 77 n.12, 90, 99-100, 119, 129 n.24, 131 n.36, 167, 170 AMMACY 31, 55-6, 71, 88- 9, 170, 185 Cu;sSC/DTXM 21, 26. 77 n. 12. 93, 99, 167

224 cognitive entrenchment

SUBJECT INDEX 1 6 - 1 7 . 29-31,

33, 41 n.22, 41 n.26, 88, 95 ConPC 27-28, 40 n.20, 51, 72 4, 91-2, 99-100, 119, 167, 170, 183 n.21 GonSC 27 28, 40 n.20, 72-4, 94, 99, 167 COMPLEX 14-15, 19, 32-3, 37 n.8, 48, 57-8, 69-71, 80-3, 98, 100, 101, 104-5, 156 n. 18, 170-1, 178-80 CONCRETE 31, 53, 71, 88 9, 98, 100. 128-9 n. 18, 169-70, 185 DET 14, 37n.5, 56-7, 69-70, 86- 7, 100, 101, 102, 104, 119. 171, 186 DISFLUEXCY 22, 60-1, 75, 96- 7, 99, 119, 123, 173 habitual meaning of the verb phrase 16, 24, 33, 77-8 n. 13, 130n.28 IDIOMATICITY 15-16. 37 n.9. 38 n.10, 52 6, 58, 63-4, 72, 87-8, 100-1, 119, 128-9 n.18, 131 n.36, 154-55 n. 11, 167-9, 188 length of the direct object 14, 19, 37 n.6-7, 57-9, 66 n.17, 70-1, 83-5, 98, 100, 101, 119, 128-9 n.18, 19, 131 n.36, 151, 170-1, 178-80 LENGTHS, see length of the direct object LENGTHW, see length of the direct object LM 19-20, 41 n.21, 51, 72-4, 89 90, 98, 104-5, 129n.22, 166, 170 news value of the direct object 18- 20, 57, 72-4, 150

NM 26. 72-4, 92, 167 OM 27, 72-4, 94 5, 128 n.18 PART-PREP 21-2, 60, 65 n. 16, 75, 96, 173 phonetic shape of the verb 13, 22- 4, 33-4, 39-40 n.16, 77-8 n.13, 130n.28 PP 21, 58-9, 65 n.14, 15. 74-5, 95-6. 101, 128-9 n.18, 130 n.27, 173 presence of a directional adverbial, see PP production and planning effects, see DlSl'LUENCY

REGISTER 36, 69, 97, 100-1, 130 n.33, 185-6, 189 semantic focus of the verb phrase 16-18, 24-5, 38 n. 12, 52, 77 n.11 semantic modification of the particle 16. 55. 72, 130n.28 stress 12-13, 17, 24-5, 38 n.11, 58, 78 n.13, 123, 130 n.28, 147, 156 n.17, 172-3 TOPM 19-20, 26-9, 39 n.l3, 51, 57, 58. 65 n.13, 66 n.17, 72-4, 91, 95, 99-100, 119, 128 n.17, 129 n.22, 131 n.36, 155 n.14, 166 7. 170 TOSM 21, 39 n.14. 26-7, 40 n.18, 19, 72-4, 93-4, 95, 99, 128 n.18, 167 TYPE 13, 19, 31, 56-7. 69-70, 85-6, 98. 100, 102, 105, 107, 129n.24. 130n.30, 131 n.36, 171-2, 174, 186

Author index*

Arnold,J. K. 22, 39 n.15, 43 n.31, 60-1, 75, 173 Arnold,.]. E. et al. 43 n.31, 47, 151 Batcs,E. 7, 9, 47, 155 n.12, 157-9, 171. 181 n.2 Behaghel, O. 38 n. 12, 59 Berg, T 160, 172, 181 n.1 Bock.J. K. 18, 40 n.19, 53, 55, 77 n.7, 120, 164, 169-70, 171, 182 n.10 Bolinger, I). 9 n.4, 10 n.4, 12, 16-18, 19-20, 24-5, 38 n.10, 52, 64 n.10, 72, 87-8, 129 n.24 Bolkesrein, A. M. 1, 27-8, 62 n.4, 74, 92 Browman. C. P. 43 n.31, 47, 56, 120. 155 n.13, 189 Chen, P. 14-15, 20-1, 26-8, 35, 39 n.14, 40 n. 19, 42 n.28, 76 n.3, 5, 84, 86, 90-4. 99, 127 n.10. 188 Cowie, A. P. 10 n.4; 15, 72 Dearie, P.I) 16-17, 28-30, 41 n.26, 50 Dell, G. S. 7, 9, 157. 159, 1 6 1 , 163-4, 182 n.5-6, 8, 10, 11, 16-8, 183n.l8 Erades, P. A.

9 n.4, 12, 13, 18

Francis, B. 43 n.31, 47, 145 6. 187 Frasei; B. 9 n.4, 13-16, 21, 22-4, 31, 33- 4, 37 n.4, 5, 40 n.17, 43 n.31, 55, 75 n.1, 82, 86 7 Givon, T. 8 9, 10 n.8. 14, 20- 1, 26, 39 n.14, 40 n.l9, 41 n.23, 42 n.28, 44, 48, 50, 57, 76 n.6, 81, 92, 125-6 n.3, 146, 155 n.12, 181 Goldberg, A. 6, 10 n.4. 53, 154 n.9- 10, 190

Cries, St. Th. 14, 16-17, 20, 26 8, 29 31, 33, 35, 56, 76 n.3, 88, 131 n.39, 191 n.2 Hawkins,.). A. 7 9, 14, 35, 54, 56, 59, 64 n.12, 65 n.14, 76 n.3, 5, 82, 84, 132, 139, 142, 146-52, 156 n.17, 20, 157, 174, 178 80, 187-8 Johnson, M. 16, 63 n.8, 72, 77 n.10 Kennedy; G. 9 n.4, 22, 43 n.33. 75 n. 1 Kruisinga, E. 9 n.4, 12, 13, 18 Lakoflf, G. 6, 16, 31, 63 n.8, 72, 77 n.10 133, 154 n.8 Lambrecht, K. 1, 47, 48-50, 62 n.3, 65 n.13, 139, 181 Langacker, R. W. 6, 38 9 n.l2, 63 n.8. 64n.10, 154 n.10 Leech, G. 43 n.31, 47, 145-6, 187 Mat-Kay, D. G 157, 160, 172. 182 n.5-6, 183 n.18, 184 n.22 Mackin, R. 10 n.l, 15, 72 McClelland,).!,. 9, 157, 182 n.6, 183 n.18 MacWhinney, B. 7, 9, 47, 155 n.12, 159, 174, 181 n.2 Moiidorf, B. 1, 120 O'Dowd

9 n.3, 42 n.27, 75 n.l

Peters 18 19, 26, 28-9, 40-1 n.21, 128 n.16 Prince 27, 42 n.27, 50. 62 n.3 Quirk et al. 9 n.4, 10 n.4, 13, 25, 57 Rissclada, R. 1, 27-8, 62 n.4, 74, 92 Rohdenburg, G. 1. 120

* Only the authors whose work is mosl significantly connected to the core of the present study anil who are mentioned several times throughout the book have been included here.

226

AUTHOR INDEX

Rumelhart, D. K. 9, 157, 182 n.6, 183n.l8

Van Dongen. W. A., Sr 12, 13, 35, 43 n.33. 65 n.15

Siewierska, A. 41 n.23, 43 n.31, 129 n.20, 146-7 StembergerJ. P 9, 157, 159-63, 168-9, 182n.5-6, 9, 183 n.12, 14, 183 n.18

Wasow, T. 15, 22, 39 n. 15, 43 n.31, 60 1 75, 124, 152, 173 Xu, X.

43 n.31, 47, 145-6, 187