Systems and Frameworks for Computational Morphology

Communications in Computer and Information Science

100

Cerstin Mahlow Michael Piotrowski (Eds.)

Systems and Frameworks for Computational Morphology Second International Workshop, SFCM 2011 Zurich, Switzerland, August 26, 2011 Proceedings

13

Volume Editors Cerstin Mahlow University of Basel Nadelberg 4, 4051 Basel, Switzerland E-mail: [email protected] Michael Piotrowski University of Zurich Binzmühlestr. 14, 8051 Zurich, Switzerland E-mail: [email protected]

ISSN 1865-0929 e-ISSN 1865-0937 ISBN 978-3-642-23137-7 e-ISBN 978-3-642-23138-4 DOI 10.1007/978-3-642-23138-4 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011933917 CR Subject Classification (1998): I.2.7

© Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

Morphological resources are the basis for all higher-level natural language processing applications. Morphology components should thus be capable of analyzing single word forms as well as whole corpora. For many practical applications, not only morphological analysis, but also generation is required, i.e., the production of surfaces corresponding to specific categories. Apart from uses in computational linguistics, there are numerous practical applications that either require morphological analysis and generation, or that can greatly benefit from it, for example in text processing, user interfaces, or information retrieval. These applications have specific requirements for morphological components, including requirements from software engineering, such as programming interfaces or robustness. With the workshop on Systems and Frameworks for Computational Morphology (SFCM) we have established a place for presenting and discussing recent advances in the field of computational morphology. In 2011 the workshop took place for the second time. SFCM focuses on actual working systems and frameworks that are based on linguistic principles and that provide linguistically motivated analyses and/or generation on the basis of linguistic categories. SFCM 2009 focused on systems for a specific language, namely, German. The main theme of SFCM 2011 was phenomena at the interface between morphology and syntax in various languages: Many practical applications have to deal with texts, not just isolated word forms. This requires systems to handle phenomena that cannot be easily classified as either “morphologic” or “syntactic.” The workshop thus had three main goals: – To stimulate discussion among researchers and developers and to offer an up-todate overview of available morphological systems for specific purposes. – To stimulate discussion among developers of general frameworks that can be used to implement morphological components for several languages. – To discuss aspects of evaluation of morphology systems and possible future competitions or tasks. Based on the number of submissions and the number of participants at the workshop we can definitely state that the topic of the workshop was met with great interest from the community, both from academia and industry. We received 13 submissions, of which 8 were accepted after a thorough review by the members of the Program Committee and additional reviewers. The peer-review process was double-blind, and each paper received four reviews. In addition to the regular papers, we had the pleasure of Lauri Karttunen giving an exciting invited talk on new features of the Finite-State Toolkit (FST).

VI

Preface

The discussions after the talks and during the demo sessions, as well as the final plenum, showed the interest in and the need and the requirements for further efforts in the field of computational morphology. We will maintain the website for this workshop at http://sfcm2011.org. This book starts with the invited paper by Lauri Karttunen (“Beyond Morphology: Pattern Matching with FST"), reporting on new developments for the Finite-State Toolkit, an enhanced version of XFST. The FST pattern matching algorithm allows applications like tokenizing, named-entity recognition, or even parsing. Then follows a paper by M¯arcis Pinnis and K¯arlis Goba (“Maximum Entropy Model for Disambiguation of Rich Morphological Tags"), describing a statistical morphological tagger for Latvian, Lithuanian, and Estonian. The authors explore the use of probabilistic models with maximum entropy weight estimation to cover the rich morphology in these languages. The paper by Benoît Sagot and Géraldine Walther (“Non-canonical Inflection: Data, Formalisation and Complexity Measures") deals with non-canonical inflection, a popular topic in linguistics, but lacking implementation. Representing inflectional irregularities as morphological rules or as additional information in the lexicon allows the implementation within the Alexina framework. The approach holds for several morphologically rich languages like French, Latin, Italian, Sorani Kurdish, Persian, Croatian, and Slovak. The following paper of Gertraud Faaß (“A User-Oriented Approach to Evaluation and Documentation of a Morphological Analyser") emphasizes the need for usercentered evaluation of morphological components. The paper by Krister Lindén, Erik Axelson, Sam Hardwick, Tommi Pirinen, and Miikka Silfverberg (“HFST—Framework for Compiling and Applying Morphologies") reports on the new version of the HFST framework, allowing users to experiment with several finite-state tools for various languages to use in open-source projects. Then follows a paper by Esmé Manandise and Claudia Gdaniec (“Morphology to the Rescue Redux: Resolving Borrowings and Code-Mixing in Machine Translation") covering morphological issues in machine translation of e-mail messages from Spanish to English when bilingual authors use borrowing, code-mixing, or code-switching. The last three papers report on morphological systems for specific languages: Arabic, Indonesian, and Swiss German. Mohammed Attia, Pavel Pecina, Antonio Toral, Lamia Tounsi, and Josef Van Genabith (“A Lexical Database for Modern Standard Arabic Interoperable with a Finite State Morphological Transducer") report on the creation of resources for modern standard Arabic. The paper by Septina Dian Larasati, Daniel Zeman, and Vladislav Kuboˇn (“Indonesian Morphology Tool (MorphInd): Towards an Indonesian Corpus") describes the development of a robust finite state open source morphology tool for Indonesian, motivated by shortcomings of existing resources. The paper by Yves Scherrer (“Morphology Generation for Swiss German Dialects") provides insights into dialectological issues for generation. Although there is a lot of research on Swiss German dialects in the field of linguistics, there is currently only very little related research in the NLP community. The contributions show that high-quality research is being conducted in the area of computational morphology: Mature systems are further developed and new systems and

Preface

VII

applications are emerging. Even though other languages are becoming more important, research in computational linguistics still focuses primarily on English, which is well known for its reduced morphology. Morphological analysis and generation are thus often regarded as being required only for the processing of some exotic languages. The papers in this book come from eight countries, discuss a wide variety of languages from many different language families, and illustrate that, in fact, a rich morphology is better described as the norm rather than the exception—proving that for most languages, as we have stated above, morphological resources are indeed the basis for all higher-level natural language processing applications. The trend toward open-source developments still goes on and evaluation is considered an important issue. Making high-quality morphological resources freely available will help to advance the state of the art and allow the development of high-quality real-world applications. Useful applications with carefully conducted evaluation will demonstrate to a broad audience that computational morphology is an actual science with tangible benefits for society. We would like to thank the authors for their contributions to the workshop and to this book. We also thank the reviewers for their effort and for their constructive feedback, encouraging and helping the authors to improve their papers. The submission and reviewing process and the compilation of the proceedings were supported by the EasyChair system. We thank Alfred Hofmann, editor of the series Communications in Computer and Information Science (CCIS), and the Springer staff for publishing the proceedings of SFCM 2011. We are grateful for the financial support given by the German Society for Computational Linguistics and Language Technology (GSCL) and the general support of the University of Zurich. June 2011

Cerstin Mahlow Michael Piotrowski

Organization

The Second Workshop on Systems and Frameworks for Computational Morphology (SFCM 2011) was organized by Cerstin Mahlow and Michael Piotrowski. The workshop was held at the University of Zurich.

Program Chairs Cerstin Mahlow Michael Piotrowski

University of Basel, Switzerland University of Zurich, Switzerland

Program Committee Bruno Cartoni Simon Clematide Axel Fleisch Piotr Fuglewicz Thomas Hanneforth Roland Hausser Lauri Karttunen Kimmo Koskenniemi Winfried Lenders Krister Lindén Anke Lüdeling Cerstin Mahlow Günter Neumann Michael Piotrowski Adam Przepiórkowski Christoph Rösener Helmut Schmid Angelika Storrer Pius ten Hacken Eric Wehrli Andrea Zielinski

University of Geneva, Switzerland University of Zurich, Switzerland University of Helsinki, Finland TiP Sp. z o. o., Katowice, Poland University of Potsdam, Germany Friedrich-Alexander University of Erlangen-Nuremberg, Germany Stanford University, USA University of Helsinki, Finland University of Bonn, Germany University of Helsinki, Finland Humboldt University Berlin, Germany University of Basel, Switzerland DFKI Saarbrücken, Germany University of Zurich, Switzerland Polish Academy of Sciences, Warsaw, Poland Institute for Applied Information Science, Saarbrücken, Germany University of Stuttgart, Germany University of Dortmund, Germany Swansea University, UK University of Geneva, Switzerland FIZ Karlsruhe, Germany

X

Organization

Additional Reviewers Johannes Handl Besim Kabashi

Friedrich-Alexander University of Erlangen-Nuremberg, Germany Friedrich-Alexander University of Erlangen-Nuremberg, Germany

Local Organization Cerstin Mahlow Michael Piotrowski

University of Basel, Switzerland University of Zurich, Switzerland

Sponsoring Institutions German Society for Computational Linguistics and Language Technology (GSCL) University of Zurich

Table of Contents

Beyond Morphology: Pattern Matching with FST . . . . . . . . . . . . . . . . . . . . . . . . Lauri Karttunen

1

Maximum Entropy Model for Disambiguation of Rich Morphological Tags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M¯ arcis Pinnis and K¯ arlis Goba

14

Non-canonical Inflection: Data, Formalisation and Complexity Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benoît Sagot and Géraldine Walther

23

A User-Oriented Approach to Evaluation and Documentation of a Morphological Analyser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gertrud Faaß

46

HFST—Framework for Compiling and Applying Morphologies . . . . . . . . . . . . . Krister Lindén, Erik Axelson, Sam Hardwick, Tommi A. Pirinen, and Miikka Silfverberg Morphology to the Rescue Redux: Resolving Borrowings and Code-Mixing in Machine Translation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Esmé Manandise and Claudia Gdaniec A Lexical Database for Modern Standard Arabic Interoperable with a Finite State Morphological Transducer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Attia, Pavel Pecina, Antonio Toral, Lamia Tounsi, and Josef van Genabith

67

86

98

Indonesian Morphology Tool (MorphInd): Towards an Indonesian Corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Septina Dian Larasati, Vladislav Kuboˇn, and Daniel Zeman

119

Morphology Generation for Swiss German Dialects . . . . . . . . . . . . . . . . . . . . . . Yves Scherrer

130

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

141

Beyond Morphology: Pattern Matching with FST Lauri Karttunen Stanford University, Palo Alto, USA              

Abstract. FST stands for Finite-State Toolkit. It is an enhanced version of the XFST tool described in the 2003 Beesley and Karttunen book Finite State Morphology. Like XFST , FST serves two purposes. It is a development tool for compiling finite-state networks and a runtime tool that applies networks to input strings or files. XFST is limited to morphological analysis and generation. FST can also be used for other applications. This paper describes the new features of the FST regular expression formalism and illustrates their use for named-entity recognition, relation extraction, tokenization and parsing. The FST pattern matching algorithm ( ) operates on a single pattern network but the network can be the union of any number of distinct pattern definitions. Many patterns can be matched simultaneously in one pass over a text. This is a distinct FST advantage over pattern matching facilities in languages such as Perl and Python. Keywords: finite-state automata, tokenization, pattern matching.

1 Creating Pattern Networks Most of the FST commands are described in the chapter on the XFST application in the Finite State Morphology book by Kenneth R. Beesley and Lauri Karttunen [1].1 The new features of FST include a special command, , for applying a pattern network to a text and many enhancements to the regular expression formalism for defining networks. 1.1 Simple Patterns The   command in FST expects two arguments: a name and a regular expression. It compiles the regular expression and binds the resulting network to the name. The name can then be used in subsequent regular expressions to refer to the network. For example,

    



   

   

defines a network containing four fruit names. The following definition creates a network for recognizing dollar amounts such as $5.10. 1

   

C. Mahlow and M. Piotrowski (Eds.): SFCM 2011, CCIS 100, pp. 1–13, 2011. © Springer-Verlag Berlin Heidelberg 2011

2

L. Karttunen

   

!

" #



$%&

#%& '( ) 

#%& *+ 

The section in square brackets defines whole numbers from 0 up to any length. The section in parentheses defines the optional decimal part of a number. In the FST regular expression formalism, round parentheses indicate optionality. To create a simple pattern network for matching fruits and prices, we first define  and   adding a final “end tag” transition:

  ,   -

.), + 

  /   -

.)/+ 

We can now make a union of the two networks for pattern matching. 0 ,   /

The  command compiles a regular expression and makes the resulting network available for application. The   construct creates a pair symbol,  , that has an epsilon (represented by zero) on the input side of the network and a closing XML tag   on the output side. The network resulting from the union recognizes fruit names and dollar amounts. The purpose of the end tags is to indicate in the output which of the two patterns was matched. If we now invoke the FST pattern matching command, , on the input An apple costs $1.05 today., the output tags apple as an instance of the  pattern and $1.05 as a  .         !$#1 2  3, 4 3, 4    3/4!$#13/4 2

Here the  function inserts an initial XML tag on the fly in front of a string identified by a closing XML tag in the pattern network. The parts of the input string that do not match any patterns are echoed into the output unchanged. Wrapping paired XML tags around matches is the default output mode of , but there are other output options. For example, if we are just interested in the locations of the matches, say, for the purpose of highlighting them in the text, we can instruct  to print just the location information ignoring everything that does not match.   %  

        !$#1 2 51 3, 4 $11!$#13/4

Here the output of  indicates the beginning byte position of the match, the number of bytes matched, the string itself and its initial tag. If the price was indicated in euros instead of dollars, û, the length of the   match would be 7 instead of 5 because the UTF-8 representation of the euro symbol C consumes three bytes instead of just one for $. The regular expression compiler in FST has a few new types of symbols not documented in [1]. For example, any collection of symbols may be defined as a LIST:  $ &

$%& 

The list  contains the digits from 1 to 9. FST comes with many system-defined lists such as   ,  , !,  , etc. An equivalent definition for list  would be

Pattern Matching with FST

 $ &  %

3

# 

The expression "# creates the symbol $"%$ that matches any of the nine digits in the list . The expression    creates the symbol $&%  $ that matches any symbol that is not a digit. The definition of '! given above can be stated more compactly using list membership symbols instead of enumerating the digits explicitly:

   

!

" #

 6 )$ &+ 6 )  +'(

) 

6 )  +*+ 

List membership symbols make it possible to represent a language in a smaller network. For example, the non-list expression ( represents the union )*+*,*-**.*/* 0*1. It compiles into a network with nine arcs, one for each digit, whereas the network for "# contains just one arc.

0

@L.1to9@

1

Fig. 1. The language 6 )$ &+

Another useful innovation in FST regular expressions is the notion of an INSERT symbol. If   is defined as a network, the expression #   creates an insert symbol $ %  $. When the  routine encounters an arc labeled $ %  $ it traverses it only if it can match a string in the   network. Taking advantage of insert symbols, we could replace our earlier definition of  and   by

  ,  , ) + -

.), +

  / , )  + -

.)/+

and create a network for matching the two patterns with 0 , ), +  , )/+

creating the network in figure 2.

0

@I.Item@ @I.Price@

1

Fig. 2. Pattern for fruit items and prices

As the example shows, a network referred to by an insert symbol may itself contain insert symbols. For example, in order to traverse the $ %  $ arc in figure 2,  has to push from the   network into the '! network.2 2

We come back to this topic in section 3.2.

4

L. Karttunen

1.2 Expanded Patterns Lists of strings can be modified to include upper case and capitalized versions by using some of the predefined FST functions. Functions such as 2 3#& take a regular expression as an argument, compile it with some modification and return the resulting network. Here are some examples of built-in functions for case conversion. 78)  2 + 78)+ 78)A 6+ = 78)+ 8)  2 + = 8)  2 + FST

994 994 994 994 994 994

:-; ? @ @ @A  : <  : < A : 2 A  < A  2 

allows the user to define new functions such as the example below.

  B08)C+ "= 8)C+  78)C+( 0 B08) +

The expression 4 3#5!  expands the list of fruit names with capitalized and upper case version of each word. Instead of apple, we now have apple, Apple and AP PLE. A definition of a particularly interesting function, 6  ! &, is given in Appendix 2. 6  !   expands apple to apple and apples. As shown below, these two functions can be nested.

  -0

)C+ B08)= /  )C++

For example,  5!  gives us the lower-case, upper-case and capitalized versions of apple, apples, peach, peaches, etc. 1.3 Relations Fruit items and dollar prices are simple examples of “named entities.” A RELATION joins two or more entities. An obvious relation for items and prices is COST . There are many ways to express the idea that X costs Y. For example, we might define a minilanguage of cost phrases as follows:

  8  /                     D       2   

Given the earlier definitions, we can now create a network for extracting or marking cost relations.

  ;E  F  0 , ), + ;E , )8  /+ ;E , )/+ -

.)8  +

With this pattern the  routine marks items and prices that are related by a cost phrase:         !$#1 2  38  43, 4 3, 4    3/4!$#13/438  4 2         !$1#  38  43, 43, 4     3/4!$1#3/438  4

Pattern Matching with FST

5

If we are primarily interested in pairing items and prices but not in the particular way the cost relation is expressed, we can create alternate definitions, 789 and 3# #9, transducers that have to match the input for  to succeed but produce no output because they have only epsilons on the output side.

  ;EG

  8  /G

#;E #8  /

With these definitions, we can produce a more minimalistic output. 0 , ), + ;EG , )8  /G+ ;EG , )/+ -

.)8  +   %  

        !$#1 2 5$H3, 4 3, 43/4!$#13/438  4

1.4 Context Conditions A pattern definition may include constraints on the context. A constraint is a condition that has to be met for a string to count as a valid match for a pattern without itself being a part of the pattern. For example, we could decide that apple and $1.05 should be marked as being in the 3# relation in examples such as Whole Foods sells an apple for $1.05. There is a class of phrases for commercial transactions that can be used as an indication that a Y is a cost for X when X and Y are separated by for. In FST this idea can be encoded in the following way;

  8 /    2      D 

  I   0 , ), + ;EG #  ;EG , )/+ -

.)8  + 68)8 / ;E )I ;E++   %  

  ;           !$#1 J$$13, 4 3, 43/4!$#13/438  4

Here the end tag of the 3# pattern, 3#, is followed by a condition on the left context: "33 # 78 : 78. The effect is that a successful match for the 3# pattern, apple for $1.05, counts as a valid 3# expression only if it is preceded by a phrase such as sells an, that is, a commerce phrase and a determiner. Context constraints can be used to disambiguate ambiguous expressions such as 1/4. 1/4 can be interpreted as a fraction (one fourth) or as a date (Jan. 4). If we construct a pattern that includes both possibilities  will always recognize 1/4 both as a fraction and as a date: 0 $K "-

.)  +  -

.)I +(   $K 3  43I 4$K3I 43  4

The network is shown in figure 3. However, in many contexts 1/4 is disambiguated by the surrounding words as in 1/4 of voters, 1/4 or before. A following preposition such as of is a positive right context, RC, for the fraction interpretation and a negative right context, NRC, for the date reading. The following FST regular expression correctly distinguises the two cases:

6

L. Karttunen

1

0

1

/

2

4

3

:0 :0

4

Fig. 3. Ambiguity of 1/4

0 $K

"-

.)  + >8) +  -

.)I + :>8) +(   $K  D  3  4$K3  4  D      $K      3I 4$K3I 4   

Figure 4 shows a network with of as a positive condition for 5  and as a negative context for :. :0 0

1

1

/

2

4

3

4

:0

NRC RC

6

""

7

o

8

f

9

5

Fig. 4. Right context conditions

Expressions like 1/4 can also be disambiguated by a preceding word. 1/4 must be a fraction in under 1/4 but a date in due on 1/4. In the following expression LC indicates that under is a positive left context for the fraction interpretation and NLC marks it as a negative left context for the date reading. 0 $K "-

.)  + 68)

 +  -

.)I + :68)

 +(   

 $K 

 3  4$K3  4    $K

 3I 4$K3I 4

Finally, left and right-context conditions may be combined with an AND or with an The following regular expression stipulates that 1/4 is tagged as a fraction if it is either preceded by under or followed by of. For the date reading, these are both negative contexts. OR.

0 $K "-

.)  + =>) 68)

 +A >8) ++  -

.)I + :I):68)

 +A :>8) ++(

The resulting network is shown in figure 5. This example demonstrates that FST can encode any context condition on regular languages that is expressible in propositional logic. In a manner similar to De Morgan’s Law, negation is pushed down to the NLC and NRC constraints: ¬(p ∨ q) is equivalent to (¬p ∧ ¬q). The context conditions are compiled as part of the pattern network in the usual way except that the subnetworks for left-context conditions, LC and NLC, are reversed. This

Pattern Matching with FST

:0 0

1

1

/

2

4

3

4

NRC

AND

6

NLC

9

""

8

""

o

11

7

f

13

u

:0

OR

5

RC

7

r

10

e

12

14

d

16

n

15

17

LC

Fig. 5. AND and OR conditions for 1/4

is because they are checked by going right-to-left in the input. Figure 6 shows the piece of the network FST compiles from "3;!