BibTEX entry of my PhD thesis:

@PhDThesis{ hartrumpf02,

author = {Sven Hartrumpf},

title = {Hybrid Disambiguation in Natural Language Analysis},

school = {FernUniversit{\"a}t Hagen, Fachbereich Informatik},

year = 2002,

address = {Hagen, Germany},

month = jun

}

and the corresponding book published by Der Andere Verlag(http://www.der-andere-verlag.de):

@Book{ hartrumpf03,

author = {Sven Hartrumpf},

title = {Hybrid Disambiguation in Natural Language Analysis},

publisher = {Der Andere Verlag},

year = 2003,

address = {Osnabr{\"u}ck, Germany},

note = {ISBN 3-89959-080-5}

}

Front matter and back matter of this book follow.

Hybrid Disambiguation in

Natural Language Analysis

Vom Fachbereich Informatikder FernUniversitat Hagen

angenommene

Dissertation

zur Erlangung des akademischen Grades einesDoktors der Naturwissenschaften

(Dr. rer. nat.)

vonDipl.-Inform.

Sven Hartrumpf

Gutachter: Prof. Dr. Hermann Helbig und Prof. Dr. Istvan Batori

Datum der mundlichen Prufung: 28. Juni 2002

Hagen, Juni 2002

c© 2002, Sven HartrumpfE-mail: hartrumpf@gmx.net, sven.hartrumpf@fernuni-hagen.deAll rights reserved

11 10 09 08 07 06 05 04 03 02 6 5 4 3 2 1

Colophon

This PhD thesis was written in the SGML DocBook format 4.2. It was trans-formed to LATEX by a tool written by the author and typeset using the LATEXtypesetting system (by Leslie Lamport) and the memoir document class (by PeterR. Wilson). The text is set 10/12pt on a 29pc measure with Computer ModernRoman designed by Donald E. Knuth.

Contents

Contents vii

List of Figures xi

List of Tables xiii

Abstract xv

Preface xvii

1 Introduction 11.1 Motivation and Theses . . . . . . . . . . . . . . . . . . . . . . . . 11.2 State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Context and Embedding of the Thesis . . . . . . . . . . . . . . . 31.5 Typographical and Notational Conventions . . . . . . . . . . . . 6

2 Foundations 72.1 Ambiguity in Natural Language . . . . . . . . . . . . . . . . . . . 72.2 Knowledge Representation Language . . . . . . . . . . . . . . . . 7

2.2.1 MultiNet Relations . . . . . . . . . . . . . . . . . . . . . . 82.2.2 MultiNet Sorts and Semantic Features in the Lexicon . . 92.2.3 MultiNet Layers . . . . . . . . . . . . . . . . . . . . . . . 18

2.3 Parsing Technology . . . . . . . . . . . . . . . . . . . . . . . . . . 232.4 Knowledge Sources . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.4.1 Corpora . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.4.2 Lexica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.5 Methods and Computational Tools . . . . . . . . . . . . . . . . . 302.5.1 Hybridization . . . . . . . . . . . . . . . . . . . . . . . . . 302.5.2 Tools for Lexical Knowledge . . . . . . . . . . . . . . . . . 302.5.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3 The WOCADI Parser 353.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.2 Basic Concepts of WCFA . . . . . . . . . . . . . . . . . . . . . . 363.3 Analyzing Simple Noun Phrases . . . . . . . . . . . . . . . . . . . 373.4 Analyzing Complex Noun Phrases . . . . . . . . . . . . . . . . . 45

viii Contents

3.5 Analyzing Simple Propositions . . . . . . . . . . . . . . . . . . . 483.6 Analyzing Complex Propositions . . . . . . . . . . . . . . . . . . 483.7 Relationship between the Parser and the Disambiguation Modules 493.8 WOCADI’s Extensions over Earlier WCFA Work . . . . . . . . . 503.9 Robust Parsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.9.1 Lexical Robustness . . . . . . . . . . . . . . . . . . . . . . 513.9.2 Grammatical Robustness . . . . . . . . . . . . . . . . . . 52

3.10 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . 533.11 Applications of the Parser . . . . . . . . . . . . . . . . . . . . . . 533.12 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4 General Disambiguation Approach 574.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574.2 Traditional Linguistic Component: Rules . . . . . . . . . . . . . 574.3 Statistical Component: Multidimensional Back-off Models . . . . 58

4.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 584.3.2 Formalization of Multidimensional Back-off Models . . . . 614.3.3 Lexicographically Ordered Dimension Abstraction Func-

tions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664.4 Comparison to Other Statistical Models . . . . . . . . . . . . . . 66

4.4.1 Back-off Models . . . . . . . . . . . . . . . . . . . . . . . 664.4.2 Maximum Entropy Models . . . . . . . . . . . . . . . . . 674.4.3 Memory-Based Learning . . . . . . . . . . . . . . . . . . . 68

5 Prepositional Phrase Disambiguation 695.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

5.2.1 PP Interpretation Rules . . . . . . . . . . . . . . . . . . . 715.2.2 Corpus Annotations for PP Disambiguation . . . . . . . . 755.2.3 Systematic Ambiguity of PP Attachment . . . . . . . . . 86

5.3 Hybrid Disambiguation Method . . . . . . . . . . . . . . . . . . . 885.3.1 Basic Ideas . . . . . . . . . . . . . . . . . . . . . . . . . . 885.3.2 Determination of Alternatives by Rules . . . . . . . . . . 905.3.3 Interpretation Disambiguation . . . . . . . . . . . . . . . 925.3.4 Attachment Disambiguation . . . . . . . . . . . . . . . . . 96

5.4 PP Disambiguation and Constituent Ambiguity . . . . . . . . . . 1035.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055.6 Multiple PP Attachment Problem . . . . . . . . . . . . . . . . . 1085.7 Achievements and Potentials for Prepositional Phrase Disam-

biguation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1095.8 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1095.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

6 Coreference Resolution 1116.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1116.2 Coreference Types . . . . . . . . . . . . . . . . . . . . . . . . . . 1136.3 Coreference Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Contents ix

6.3.1 Coreference Rules . . . . . . . . . . . . . . . . . . . . . . 1176.3.2 Corpus Annotations for Coreference Resolution . . . . . . 1186.3.3 Coreference Annotation Guidelines . . . . . . . . . . . . . 1276.3.4 Minimal Markables for Fairer Evaluation . . . . . . . . . 134

6.4 Coreference Resolution Method . . . . . . . . . . . . . . . . . . . 1356.4.1 Algorithm Overview . . . . . . . . . . . . . . . . . . . . . 1356.4.2 Coreference and Semantic Molecules . . . . . . . . . . . . 1406.4.3 Identifying Markables . . . . . . . . . . . . . . . . . . . . 1436.4.4 Limiting the Search Space with Coreference Rules . . . . 1456.4.5 Searching for the Correct Partition of Markables . . . . . 1466.4.6 Filtering Constraints for Coreference Partitions . . . . . . 1466.4.7 Probabilities as Scores for Coreference Partitions . . . . . 1536.4.8 Bonus Factors for Coreference Partitions . . . . . . . . . . 155

6.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1576.6 Parallel Corpora for Efficient Annotation and System Comparison 1606.7 Stepping Beyond Identity . . . . . . . . . . . . . . . . . . . . . . 1606.8 Achievements and Potentials for Coreference Resolution . . . . . 1616.9 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1626.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

7 Word Sense Disambiguation 1657.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1657.2 Word Sense Disambiguation for PPs . . . . . . . . . . . . . . . . 166

7.2.1 Basic Ideas . . . . . . . . . . . . . . . . . . . . . . . . . . 1667.2.2 Corpus Annotations for Word Sense Disambiguation in

PP Contexts . . . . . . . . . . . . . . . . . . . . . . . . . 1687.2.3 Combination with the PP Disambiguation Module . . . . 168

7.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1697.4 Achievements and Potentials for Word Sense Disambiguation . . 1717.5 Situation Sense Disambiguation . . . . . . . . . . . . . . . . . . . 172

7.5.1 Basic Ideas . . . . . . . . . . . . . . . . . . . . . . . . . . 1727.5.2 Disambiguation Method . . . . . . . . . . . . . . . . . . . 172

7.6 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1737.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

8 Extensions 1758.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1758.2 Subordinate Clause Attachment and Interpretation . . . . . . . . 1758.3 Relative Clause Attachment . . . . . . . . . . . . . . . . . . . . . 1768.4 Resolution of Ellipsis in Coordinative Constructions . . . . . . . 1768.5 Genitive NP Attachment and Interpretation . . . . . . . . . . . . 1778.6 Scope of Quantifiers . . . . . . . . . . . . . . . . . . . . . . . . . 1788.7 Adverbial Attachment . . . . . . . . . . . . . . . . . . . . . . . . 178

x Contents

9 Conclusions 1819.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1819.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1829.3 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

Bibliography 185

Index 197

List of Figures

1.1 Context of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1 Relations and functions in MultiNet without lexrel, semrel, and *funsubhierarchies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Relations in MultiNet: lexrel subhierarchy . . . . . . . . . . . . . . . 92.3 Relations in MultiNet: semrel subhierarchy . . . . . . . . . . . . . . 102.4 Functions in MultiNet . . . . . . . . . . . . . . . . . . . . . . . . . . 112.6 Sort hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.8 Subtypes of entity without the con-object, abs-object, and situation

subhierarchies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.9 Subtypes of con-object . . . . . . . . . . . . . . . . . . . . . . . . . . 162.10 Subtypes of abs-object . . . . . . . . . . . . . . . . . . . . . . . . . . 172.11 Subtypes of situation . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.14 The feature type lay . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.15 Values for layer features . . . . . . . . . . . . . . . . . . . . . . . . . 222.16 Hierarchy of knowledge types (ktype) . . . . . . . . . . . . . . . . . . 232.17 Graphical semantic network for sentence (1) . . . . . . . . . . . . . . 252.18 Textual semantic network for sentence (1) . . . . . . . . . . . . . . . 262.19 Lexical entry for drangen.1.1 in IBL format . . . . . . . . . . . . . . 272.20 Expanded lexical entry for drangen.1.1 . . . . . . . . . . . . . . . . . 282.21 Formal syntax of typed feature structures . . . . . . . . . . . . . . . 29

3.1 Transition diagram for WCFM’s states during NP analysis . . . . . 373.3 Simplified opening act of the WCF adv . . . . . . . . . . . . . . . . 393.4 Architecture of the WOCADI parser (data flow diagram) . . . . . . 403.5 Simplified algorithm for level 1 of the WCFM . . . . . . . . . . . . . 423.7 Contents of the AM after step 3 (top box), 6 (middle box), and 23

(bottom box) with parts of feature structures . . . . . . . . . . . . . 443.8 Semantic network for sentence (5) . . . . . . . . . . . . . . . . . . . 463.9 Semantic network for sentence (6) . . . . . . . . . . . . . . . . . . . 473.10 Modules of the WOCADI parser . . . . . . . . . . . . . . . . . . . . 54

4.1 Formal top-level syntax of interpretation rules . . . . . . . . . . . . . 584.2 Dimensions in a multidimensional back-off model for PP attachment 61

5.1 Examples of PP interpretation rules for uber . . . . . . . . . . . . . 72

xii List of Figures

5.2 Formal syntax of interpretation rules . . . . . . . . . . . . . . . . . . 735.3 Example of a coreference rule . . . . . . . . . . . . . . . . . . . . . . 745.4 Interpretation rules and corpus examples of auf -PPs (part 1/2) . . . 755.5 Interpretation rules and corpus examples of auf -PPs (part 2/2) . . . 765.6 Interpretation rules and corpus examples of aus-PPs (part 1/2) . . . 775.7 Interpretation rules and corpus examples of aus-PPs (part 2/2) . . . 785.8 Interpretation rules and corpus examples of bei -PPs . . . . . . . . . 795.9 Interpretation rules and corpus examples of uber -PPs (part 1/2) . . 805.10 Interpretation rules and corpus examples of uber -PPs (part 2/2) . . 815.11 Interpretation rules and corpus examples of vor -PPs (part 1/2) . . . 825.12 Interpretation rules and corpus examples of vor -PPs (part 2/2) . . . 835.13 Interpretation rule and corpus example of wegen-PPs . . . . . . . . . 835.19 Architecture of the PAIRUDIS module (data flow diagram) . . . . . 915.20 Disambiguation algorithm for PP attachment and interpretation . . 93

6.4 Coreference rules for personal pronouns . . . . . . . . . . . . . . . . 1216.5 Coreference rules between nouns . . . . . . . . . . . . . . . . . . . . 1226.6 Coreference rules for pronouns other than personal pronouns . . . . 1236.7 Coreference rules for determiners . . . . . . . . . . . . . . . . . . . . 1246.8 Coreference rules for special cases . . . . . . . . . . . . . . . . . . . . 1256.9 Coreference annotations in SGML format for sentence (33) . . . . . 1266.10 Keys for corpus text 19990201polit f as stored in the coreference keys

file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1276.11 Keys for corpus text 19990201polit f in graphical form . . . . . . . . 1286.13 Bell numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1366.14 Coreference resolution algorithm . . . . . . . . . . . . . . . . . . . . 1376.15 Architecture of the CORUDIS module (data flow diagram) . . . . . 1396.16 Molecule and nonmolecule relevant to example (78) . . . . . . . . . . 1426.17 Molecule and nonmolecule relevant to example (79) . . . . . . . . . . 1446.20 Coreference distances in the corpus measured in markables . . . . . 1506.21 Coreference distances in the corpus measured in sentences . . . . . . 1516.22 Coreference distances in the corpus measured in paragraphs . . . . . 152

List of Tables

2.5 Description of semantic relations important in this work . . . . . . . 122.7 Semantic Boolean features in the lexicon . . . . . . . . . . . . . . . . 142.12 Entity types in the lexicon (part 1/2) . . . . . . . . . . . . . . . . . 192.13 Entity types in the lexicon (part 2/2) . . . . . . . . . . . . . . . . . 202.22 Important features in the lexicon . . . . . . . . . . . . . . . . . . . . 29

3.2 Word class functions in the WOCADI parser . . . . . . . . . . . . . 383.6 Analysis process of sentence (2) on level 1 . . . . . . . . . . . . . . . 43

5.14 Problem cases for the PP disambiguation module . . . . . . . . . . . 855.15 Attachment data from the evaluation corpus . . . . . . . . . . . . . 865.16 Attachment-interpretation matrix for sentence (24) (after step 1) . . 905.17 Attachment-interpretation matrix for sentence (24) (after step 2) . . 905.18 Attachment-interpretation matrix for sentence (24) (after step 3) . . 905.21 Results of PP interpretation rules for (correct) mothers . . . . . . . 945.22 Statistical data for PP interpretation (aus) . . . . . . . . . . . . . . 975.23 Statistical data for PP attachment (aus) . . . . . . . . . . . . . . . . 1015.24 Results of hybrid PP disambiguation for individual prepositions . . . 1065.25 Results of hybrid PP disambiguation for all evaluated prepositions . 106

6.1 Features in coreference rules . . . . . . . . . . . . . . . . . . . . . . . 1176.2 Predicates in coreference rules (part 1/2) . . . . . . . . . . . . . . . 1196.3 Predicates in coreference rules (part 2/2) . . . . . . . . . . . . . . . 1206.12 Characteristics of the annotated texts . . . . . . . . . . . . . . . . . 1296.18 Results for markable identification . . . . . . . . . . . . . . . . . . . 1456.19 Number of antecedent candidates per identified markable . . . . . . 1456.23 Statistical data for coreference . . . . . . . . . . . . . . . . . . . . . 1566.24 Coreference resolution results . . . . . . . . . . . . . . . . . . . . . . 158

7.1 Word sense ambiguities for mothers and alternative mothers of PPs 1677.2 Word sense ambiguities for sister NPs of prepositions . . . . . . . . . 1687.3 Word sense disambiguation results of PAIRUDIS/WSD for mother

constituents of prepositional phrases . . . . . . . . . . . . . . . . . . 1697.4 Word sense disambiguation results of PAIRUDIS/WSD for sister

consituents of prepositions . . . . . . . . . . . . . . . . . . . . . . . . 170

xiv List of Tables

7.5 Improvements of hybrid PP disambiguation by word sense disam-biguation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

7.6 Error classes for PP disambiguation and word sense disambiguation 1717.7 Solution classes for word sense disambiguation . . . . . . . . . . . . 172

Abstract

This PhD thesis proposes, formalizes, and evaluates a new hybrid disambigua-tion method for ambiguity problems in natural language processing (NLP): rule-centered multidimensional back-off. The first eight chapters are summarized inthe following eight paragraphs in turn.

As an introduction, the context and the embedding of the work are described.Three main theses are formulated and the work is motivated.

A general classification of ambiguities is given and the concept of hybridiza-tion is introduced. The most important parts of the embedding of the thesisare described: the employed semantic representation formalism (MultiNet), theWOCADI parser applied for the disambiguation modules, the knowledge sourcesrequired by the parser (corpora and lexica), and the tools for lexical knowledge.Basic concepts of evaluation techniques are explained because the disambigua-tion method is thoroughly evaluated for three paradigmatic ambiguity problemsin NLP using annotated corpora of German newspaper articles.

The WOCADI parser is described in more detail because it is required — todifferent extents (phrase or sentence parsing) — for the disambiguation modules.Its architecture, some principles, and example results are presented.

Before approaches for specific disambiguation problems are developed, the gen-eral approach is introduced. The solution is hybrid and combines interpretationrules expressing valuable linguistic knowledge and statistics on top of the ap-plicability of these rules for annotated corpora. For the statistical part, a newstatistical model is defined: multidimensional back-off models that extend theconcept of traditional back-off models considerably.

The three ambiguity problems, whose treatment together with the general dis-ambiguation approach is the scientific center of the thesis, are prominent examplesthat span a whole range of disambiguation problems. First, the problem of prepo-sitional phrase attachment and interpretation (a structural ambiguity) is tackledwith an instantiation of the general disambiguation approach and the additionof bonus factors from syntax and semantics. The resulting PAIRUDIS system isevaluated on a corpus.

Second, the problem of coreference resolution (a referential ambiguity) is de-scribed and a hybrid solution with rules and disambiguation statistics, the CO-RUDIS system, is proposed and evaluated. The evaluation includes additionalbonus factors like syntactic or semantic parallelism of constituents.

Third, the problem of word sense disambiguation (WSD; a lexical ambiguity)is treated for a subproblem, namely WSD for words in prepositional phrase con-

xvi Abstract

texts. The resulting system is evaluated and the improvements achieved for thePAIRUDIS system are determined.

Finally, further ambiguity problems in NLP are briefly introduced. For themajority of them, a hybrid solution with a rule-centered multidimensional back-off model is sketched.

Keywords: computational linguistics, natural language understanding, rule-centered multidimensional back-off, prepositional phrase disambiguation, corefer-ence resolution, word sense disambiguation.

Preface

I would like to thank many people who helped me finish my PhD thesis and finishit the way it went. My doctoral advisor Prof. Dr. Hermann Helbig was alwaysopen to discuss the ambiguity problems described in this work. He fostered mywriting of this thesis with open-minded and intense discussions about a great va-riety of neighboring areas like knowledge representation (especially his paradigmof Multilayered Extended Semantic Networks — MultiNet), parsing, natural lan-guage processing applications, etc. I am also grateful to my second referee, Prof.Dr. Istvan Batori, for his insightful comments. He always tried to open my eyesfor longer perspectives and wider interconnections in the fields of natural languageprocessing and linguistics.

My colleagues from the chair of Applied Computer Science VII at the Uni-versity of Hagen (Praktische Informatik VII, FernUniversitat Hagen) helped mewith fruitful discussions and cooperations: Andreas Mertens, Rainer Osswald,and Marion Schulz, in the areas of computational linguistics; Carsten Gnorlichand Johannes Leveling in the areas of natural language interfaces and knowledgerepresentation. Rainer Osswald also gave me thorough feedback on several earlierversions of this thesis.

Furthermore, I want to thank all the student workers who contributed help-ful linguistic resources, programs, and comments: Dieter Staudt, Markus Greif,Sara Hakemi, Johannes Maeso, Steffen Marthen, Robert Standera, and ThorstenTollner. Monika Kleinen and Christoph Doppelbauer helped in technical andnontechnical organization issues.

Ingo Glockner provided very useful comments starting with the cooperativeresearch project Virtual Knowledge Factory (Virtuelle Wissensfabrik), a projectof the German federal state of Nordrhein-Westfalen.

I would also like to mention the organizers and participants of RANLP-97 inTzigov Chark, Bulgaria, because this conference encouraged me to put even moreemphasis on practical systems that are built from corpus data and rigidly evaluatedon corpora containing real-world texts. This seems central to me if one wants tofollow the language engineering approach to computational linguistics.

Manuel Serrano, the author of the Bigloo Scheme compiler, and Felix Winkel-mann, the author of the Chicken Scheme compiler, helped me to make the WO-CADI parser used in this thesis practical, robust, and efficient. They alwaysreacted very kindly to my sometimes presumptuous requests with improvementsuggestions, compiler extensions, or compiler bug fixes.

xviii Preface

My parents facilitated my way to university with a happy and protected child-hood. I am very grateful for that.

The person who suffered most during thesis preparation was certainly my wifeTanja. I would like to thank her for loving and patient support. She made my lifeso much different throughout the years we share. Finally, I want to mention ourson Nicolas who joined the dissertation effort during the first months of his lifeand is now looking forward to see his father more often.

Sven HartrumpfHagen, Germany

June 2002

Chapter 1

Introduction

1.1 Motivation and Theses

The main motivation for this PhD thesis is the fact that ambiguity is omnipresentin natural language processing (NLP). Most cases of ambiguity are not recognizedby native speakers but are only due to the current insufficient state of methodsin computational linguistics that are far from reaching the wonderful abilities ofpeople to speak and understand with ease and virtuosity.

This work maintains three main theses.

Thesis A The seamless integration, not a mere juxtaposition, of a linguistic rule-based component and a statistical component that relies on knowledge ex-tracted from text corpora can improve disambiguation results significantlycompared to purely rule-based or purely statistical approaches.

Thesis B The new statistical model in this thesis, the multidimensional back-offmodel, suits a hybrid approach as described in Thesis A very well.

Thesis C The hybrid approach with rules and multidimensional back-off mod-els (rule-centered multidimensional back-off) is general enough to besuccessfully applied for ambiguity problems from many diverse areas in NLP.

These claims are supported by theoretical arguments and empirical evaluationsfor three prototypical disambiguation problems spanning a whole range of disam-biguation problems: prepositional phrase attachment and interpretation, corefer-ence resolution, and finally word sense disambiguation. The empirical evaluationsare enabled by the complete implementation of all three disambiguation mod-ules and the implementation of a semantically oriented parser for the Germanlanguage.

Why hybrid disambiguation? Because hybridity protects against the hybristhat a pure traditional rule system or a pure statistic approach can achieve thebest results for natural language understanding (NLU). Hybridization is a pop-ular methodology in many sciences. The influential book The Balancing Act –Combining Symbolic and Statistical Approaches to Language edited by Klavansand Resnik (1996) summarizes a first state of the art for hybridization in compu-tational linguistics.

2 Introduction

Statistical approaches often do not rely on linguistic data like syntax and se-mantics. So one can build large-scale (wide coverage) systems efficiently and portsystems from one domain to another without starting from scratch. But duringrecent years deep linguistic knowledge bases like lexica with semantic informationhave grown to interesting sizes so that it is promising not to throw these awaybut to throw these in the arena of statistics. Adding statistics in effective ways tomore traditional rule systems involving linguistic ambiguities is the overall themeof this work.

The hybridization aimed at is a thorough integration and fusion of rules andstatistics where each profits from the advantages of the other and each compen-sates for the disadvantages of the other as far as possible. Just putting two differentsystems side by side and letting an arbiter combine the decisions of the two is notconsidered enough for a considerable improvement in the area of automatic dis-ambiguation. The hybrid disambiguation modules developed in this thesis shouldoutperform purely rule-based methods and purely statistical methods.

Sometimes it is a helpful introduction to say what one should not expect fromreading a piece of scientific literature. Here is such a hint. It is neither a complexnew statistical paradigm, nor stepping beyond established grammar paradigms likeHead-Driven Phrase Structure Grammar (HPSG) or Lexical Functional Grammar(LFG).

Nevertheless, there are several important points one can expect from this work:a powerful new combination of valuable linguistic knowledge and statistics thatimproves on its components. Its application for diverse important problems ofNLU is promising. Its strengths and weaknesses are extensively empirically eval-uated proving successes and identifying directions for further research.

1.2 State of the Art

Most theses should first review the state of the art in the relevant area of re-search. As I cover three quite diverse areas, I decided to let the state of the artreviews mainly stay in their corresponding chapters. So, this section is just a cross-reference to three sections: for prepositional phrase disambiguation see section 5.8,for coreference resolution see section 6.9, and for word sense disambiguation seesection 7.6.

Here is only a short overview. The first approaches to disambiguation are mainlyrule-based. Later approaches rely on learning: supervised learning achieves thebest results but requires annotated corpora; in recent years, unsupervised learningbecame more popular because it does away with labor-intensive manual annota-tion. The performance of unsupervised methods comes close to the performanceof supervised methods, but there remains a significant advantage of supervisedmethods for most disambiguation problems in NLP. A thorough and rich intro-duction into learning approaches and statistical NLP is given by Manning andSchutze (1999).

Besides this trend from rule-based system to statistical systems, a separatetrend appears to be hybridization: the combination of different approaches inorder to increase the system performance (correctness, recall, precision, etc.) on

1.3. Chapter Overview 3

NLP tasks. An overview of some hybrid systems in NLP is provided by Klavansand Resnik (1996)

For two of the three disambiguation problems discussed in detail in this thesis,there exist special issues of the journal Computational Linguistics. Number 4of volume 27 is devoted to computational anaphora resolution, and number 1 ofvolume 24 covers word sense disambiguation.

1.3 Chapter Overview

In this first chapter, the topic of this PhD thesis is motivated and the mainindividual theses are described. The context and embedding of the thesis at theChair of Applied Computer Science VII at the University of Hagen is characterized.Typographical and notational conventions are explained.

The following chapter provides foundations for many areas vital in subsequentchapters: types of ambiguity in natural language, the knowledge representationlanguage used throughout this work, parsing technology, main knowledge sourceslike annotated corpora and lexica, and methods and computational tools.

Chapter 3 presents the WOCADI parser which is applied — to different extent— for integrating and evaluating the disambiguation modules PAIRUDIS (Prepo-sitional phrase Attachment and Interpretation with RUles and DIsambiguationStatistics, CORUDIS (COreference RUles with DIsambiguation Statistics), andPAIRUDIS with WSD (Word Sense Disambiguation) described in chapters 5 to 7.The four chapters 4 to 7 form the scientific center and the main contribution ofthis work.

The statistical model adapted in all disambiguation modules, the multidimen-sional back-off model, is introduced in its general form in chapter 4.

Then, three paradigmatic ambiguity problems are treated with a hybrid disam-biguation module: prepositional phrase attachment and interpretation (chapter 5),coreference resolution (chapter 6), and word sense disambiguation (chapter 7).Each of these three chapters contains an introduction, description of the detailsand specializations of each disambiguation method, an evaluation, related work,and conclusions.

Chapter 8 describes many other prominent ambiguity problems and indicateshow they can be tackled by the hybrid method exemplified before.

The last chapter contains a summary, a description of the scientific contributionsin the thesis, and perspectives for future work.

The back matter of this thesis consists of the bibliography and the index con-taining subjects, citations, and acronyms.

1.4 Context and Embedding of the Thesis

The disambiguation modules I developed for this thesis and the parser I imple-mented and improved during thesis preparation should not be seen in isolation.They all build on many other components like formalisms, linguistic resources,or programs that were authored by colleagues at the chair of Applied Computer

4 Introduction

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Science VII at the University of Hagen (Praktische Informatik VII, FernUniver-sitat Hagen). The disambiguation modules and the parser rely on these othercomponents, but also provide feedback, be it empirical data for formalisms, crosschecking for linguistic resources, etc. Figure 1.1 tries to illustrate informally theserelations around the disambiguation modules and the parser.

1.4. Context and Embedding of the Thesis 5

Some of the main dependency or usage relations from or to the complex ofparser plus disambiguation modules can be elaborated as follows:

1. Semantic representation formalism MultiNet (Hermann Helbig, see also sec-tion 2.2). The WOCADI parser and the disambiguation modules adoptedthis formalism as the common language for semantic representations. In re-turn, the application of MultiNet in these programs provides feedback aboutthe practical implications of the massive and pervasive use of MultiNet.

2. Word Class Function Analysis (WCFA) paradigm (Hermann Helbig, seealso chapter 3). The paradigm of WCFA (Helbig, 1986, 1994) and a firstimplementation for the German language provided a solid ground and manybuilding blocks for my further development of WCFA and a new implemen-tation with substantial extensions, the WOCADI parser. The extensionsin WOCADI and the empirical results from millions of different parses hasproduced ideas for refinements of WCFA.

3. HaGenLex (concept: Marion Schulz, Sven Hartrumpf; entries: many col-leagues and student workers; see also section 2.4.2). This lexicon with itsrich syntactic and semantic content is vital for the parser (and indirectly forthe disambiguation modules too). As feedback, the parser is applied to spoterrors in the lexicon, e.g. by trying to parse the example sentences of lexi-cal entries, by parse success rate comparisons, etc. There are also projectsthat put this idea one step further to a bootstrap approach. Staudt (2002)develops a tool that learns noun semantics for unknown words by using WO-CADI’s parse results. The learned semantic refinements for entries can thenbe used to improve parse results and the whole process can start again.

4. IBL formalism (section 2.4.2). This lexicon formalism is applied for Ha-GenLex. This lexicon is the first large-scale application of IBL and helpedidentify strengths and deficiencies of IBL and its implementation.

5. LIA (Rainer Osswald, Marion Schulz; see section 2.5.2). The workbench forlexicographers LIA increases the speed and quality of entering and main-taining lexemes in HaGenLex.

6. MWR (Carsten Gnorlich, see section 2.5.2). This editor with a graphicaluser interface allows to enter, modify, view, combine, etc. semantic networks.Some of MWR’s operations are helpful for investigating WOCADI results,whose semantic part is a semantic network of the MultiNet formalism (amulti-net). In the opposite dependency relation, MWR provides a directlink to the parser so that instead of manually entering networks one cantype a corresponding sentence and MWR receives the semantic networkrepresentation for the sentence from WOCADI.

7. Presentation component for parse results. The parser uses this componentto visualize its results for humans. So, these results can be checked, furtherevaluated, etc.

6 Introduction

8. Application: NLI (natural language interface) for databases (Johannes Lev-eling: an NLI for bibliographic databases on the Internet; Carsten Gnor-lich: an NLI for SQL databases containing bibliographic information; ErnstHoffmann: an NLI for SQL databases containing product information; seesection 3.11). The parser development profited from the ongoing use of itsresults in these natural language interfaces. Modifications of multi-nets de-livered as parse results were triggered by this feedback. The applications ofcourse critically rely on WOCADI’s parse results.

9. Application: NLI for multimedia databases (see section 3.11). The twodependencies mentioned in item 8 also apply here.

To sum up, the close and deep semantic interaction of systems, tools, andformalisms characterized above is a big advantage for the disambiguation methodsdescribed in this thesis.

1.5 Typographical and Notational Conventions

Throughout this work, different kinds of content has been marked up differently.So, these different kinds of content are rendered in a consistent way to ease reading.First, the language related conventions are explained, then the more technicallyoriented conventions.

Natural language expressions that are cited or discussed appear in italics, e.g.the preposition in is highly ambiguous. Names of concepts underlying naturallanguage expressions (or IDs of word readings or senses) appear sans serif, e.g.the concepts for two readings of the English noun field : field.1.1 and field.1.2.Foreign language expression like a priori and sans serif in the preceding sentenceare also set in italics. Important terms are in bold face on their first occurrencelike lexical ambiguity in section 2.1. Mathematical expressions, variables, etc. arerendered in italics: f , C, etc.

The conventions for technical material in this work are all related to featurestructures. Features like orth (orthography) or the feature path syn cat (syn-tactic category) are in small capitals, (feature) values (or types) like acc (thesyntactic case accusative) appear in italics, which are used for several things butthe context should clarify what the marked up material refers to.

As the object language of most linguistic examples in this thesis is German andnot English there is a need for glosses. Each gloss starts with the original sentencein German, followed by a word-to-word translation into English (or a so-calledinterlinear translation), and ends with an English translation (omitted if identicalto the word-to-word translation). This is illustrated by example (1) in section 2.3.Linguistic examples are numbered with a natural number in parentheses as in thesentence before, while equations are numbered with the chapter number and arunning number in parentheses like equation (2.1) in section 2.5.3.

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Index

Abney (1996), 36abstracted representation, 62abstraction function, 62abstraction level, 62accumulation level function, 64, 95, 99,

107, 154, 160adverbial attachment, 178AM, see analysis memoryambiguity, 7

lexical, 7, 165–167, 182referential, 7, 182scopal, 7structural, 7, 182

analysis memory, 36anaphor, 112

lexical, 114anaphora, 114

direct, 115functional, 115indirect, 115

anaphora resolutionpronominal, 114

anaphoric, 113antecedent, 111, 112Aone and Bennett (1996), 162apposition, 126, 131Apresjan (1974), 14

Baayen et al. (1995), 31back-off

alternative, 60continuous, 95detail, 60discontinuous, 95early, 63late, 63

back-off direction function, 63–66, 95,99, 154, 183

back-off modelmultidimensional, 60

Baldwin (1997), 162Barker et al. (1997), 103Basili et al. (1997), 107Batori (1969), 173Bell (1934), 135Bell number, 135Berger et al. (1996), 67Bierwisch (1983), 14bonus factor, 57, 89, 100, 102, 137, 141,

146, 155, 157Brachman (1978), 8Brachman and Schmolze (1985), 8bridging, 114, 115

attachment-object, 116attribute-object, 116element-set, 116part-whole, 116, 118possessor-object, 116subset-set, 116thematic role, 116

bridging reference, see bridgingBrill and Resnik (1994), 109, 183Broker et al. (1994), 55Broker et al. (1997), 55Buitelaar (1998), 14Byron (2001), 159

c-command, 74, 118, 119, 121, 123, 125,137

Cardie and Wagstaff (1999), 163Carpenter (1992), 18, 27cataphor, 112, 113cataphoric, see cataphor

198 Index

CELEX, 31, 51chunk, see parse modeClark (1977), 114co-indexation, 111COLEX, 48Collins and Brooks (1995), 59, 67, 107,

110completing act, 35compound analysis, 31, 50, 51, 84, 104,

155concept, 8concept family, 14, 166conclusion, 58conjunction reduction, 176Connolly et al. (1994), 162constraint

constituent, 117distance, 146interconstituent, 117semantic compatibility, 146, 147

coordinationof NPs, 134

coreference, 111attribute-value, 115identity, 112intersentential, 112intrasentential, 112name-object, 115part-whole, 112

coreference alternative, 153coreference chain, 111, 126, 127, 129,

140, 147, 148, 155, 157, 161coreference key, 111, 124, 126, 127coreference link, 111Coreference Rules with Disambiguation

Statistics, see CORUDIScoreferential, 111correlate

sentence, 130subject, 130

CORUDIS, 3, 50, 115, 134, 135, 138,139, 141, 155, 160–162

Crain and Steedman (1985), 109cross-validation, 32

Daelemans et al. (2002), 31, 68, 108,173

Dagan and Itai (1990), 162data flow diagram, 40, 91, 139data representation model, 62Davies and Poesio (1998), 129Day et al. (1997), 31definite

inferred, 116development corpus, 140Diab and Resnik (2002), 173dimension abstraction function, 62, 66,

96, 183dimension combination function, 62, 66,

95, 99, 154dimension levels, 63, 94, 98, 106, 107,

153, 159disambiguation, 7discourse-new, 118, 135, 137, 138discourse-old, 135distance

candidate, 146markable, 146paragraph, 146sentence, 146

dotted type, 14

Eimermacher (1988), 55endophoric, 113equivalence closure, 125Erku and Gundel (1987), 115error rate

apparent, 31true, 32

exophoric, 113exponential number, 135

F-measure, see F-scoreF-score, 33, 158, 160, 163facet, 14, 74, 119, 140, 143, 155, 161,

168feature

amother, 76animal, 14animate, 14artif, 14axial, 14c, 76c-id, 71, 76

Index 199

card, 21, 24case, 71, 155cat, 117compat-r, 48, 90entity, 14, 18, 62, 110, 117, 141,

143, 147, 173etype, 9, 21, 24, 71, 72, 117, 141,

143example, 29f, 74fact, 9, 21, 24gend, 72, 117, 148gener, 9, 21, 24geogr, 14human, 14id, 120, 129info, 14instit, 14instru, 14ktype, 18lay, 141, 143, 147lay quant, 147lay refer, 41, 147lay varia, 147legper, 14mental, 14, 18method, 14min, 134, 135molec, 14morph, 29morph base, 29mother, 76mother-c-id, 105, 168movable, 14num, 29, 71, 72, 117orth, 6p-status, 36perf-aux, 28pers, 29, 72, 117potag, 14proper, 117quant, 21, 24ref, 120, 129refer, 21, 24, 117, 147select, 29sem, 29semsel, 29

semsel c-id, 29semsel compat-r, 29, 93semsel select, 29semsel select . . . oblig, 29semsel select . . . rel, 29semsel select . . . sel, 29semsel sem, 29, 143semsel sem entity, 14, 29, 72,

140, 143semsel sem entity sort, 14, 29semsel sem lay, 29, 72, 143semsel sem lay fact, 175semsel sem net, 29, 41sentence-id, 117sister, 75sister-c-id, 105, 168sort, 9, 14, 18, 24, 71, 72, 94, 97,

98, 100, 101, 117, 173spatial, 14syn, 28, 29syn agr, 29, 72syn cat, 6, 29syn mod, 175syn perf-aux, 28, 29syn sep-prefix, 29syn tem, 175thconc, 14type, 129varia, 21, 24

feature path, 28feature structure

typed, 27first-mention, 135fold, 32Franz (1996a), 110Franz (1996b), 110Fujii et al. (1998), 173

GermaNet, 110, 155, 163, 173Gnorlich (2002), 31Gomez et al. (1997), 110Grosz et al. (1995), 157, 163

Haegeman (1994), 118HaGenLex, 27, 30, 72Hahn and Strube (1996), 115Harabagiu and Maiorano (2000), 163

200 Index

Hart et al. (1968), 149Hartrumpf (1994), 27Hartrumpf (1996), 27Hartrumpf (1999), 71, 85Hartrumpf (2000), 27Hartrumpf (2001), 113Hartrumpf and Helbig (2002), 51Hartrumpf and Schulz (1997), 27Hartrumpf et al. (2003), 27Helbig (1986), 5, 23, 35Helbig (1994), 5, 23, 35Helbig (2001), 8, 14Helbig and Hartrumpf (1997), 36Helbig et al. (1994), 108, 109Helbig et al. (2000), 8, 53Hemforth (1993), 36Hindle and Rooth (1993), 86, 88, 92,

109Hirschman and Chinchor (1997), 112,

118, 129, 131, 176Hirschman et al. (1998), 120Hirst (1987), 109, 168homography, 165homonymy, 165homophony, 165

IBL, see Inheritance Based Lexicon For-malism

Ide et al. (1996), 24, 120Inheritance Based Lexicon Formalism,

5, 27

Kamp and Reyle (1993), 8Katz (1987), 67Kehler (1997), 162Kelsey et al. (1998), 53Kennedy and Boguarev (1996), 162key, 31Kimball (1973), 108Klavans and Resnik (1996), 1, 3, 30Knoll et al. (1998a), 8, 53Knoll et al. (1998b), 53knowledge type, 21

Lappin and Leass (1994), 162layer feature, 9, 18, 24, 29, 51, 55learning

memory-based, 58, 68, 106, 108, 110Leveling and Helbig (2002), 8, 53lexicon

deep, 31flat, 31

Mann and Thompson (1988), 23Manning and Schutze (1999), 2, 62markable, 112

missing, 140nonreferable, 135relevant, 127, 129, 134, 140, 143,

145, 157, 159spurious, 140

Markert and Hahn (1997), 162Marthen (2002), 54, 115maximum likelihood estimate, 59McRoy (1992), 173Mehl et al. (1998), 107, 109Merlo et al. (1997), 110MESNET, 51Message Understanding Conference, 112,

115, 118, 126, 127, 129–132,134, 160, 162, 163, 176

metonymy, 132, 162Minsky (1975), 8Mitkov (1995), 162Mitkov (1997), 162Mitkov (1998a), 159Mitkov (1998b), 162Mitkov (1999a), 113, 163Mitkov (1999b), 162Mitkov (2001), 163MLE, see maximum likelihood estimatemolecule, 14, 74, 94, 119, 140, 142, 144,

155, 161, 166, 168mother, 71

alternative, 76, 135, 166candidate, 76, 84, 86, 88, 90, 92,

98, 103, 108mother constituent, see motherMUC, see Message Understanding Con-

ferenceMUC-7 (1998), 160multi-net, 5, 8, 9, 23, 24, 31, 50, 53, 55,

118multidimensional back-off

Index 201

rule-centered, 1, 57, 71, 110, 160,167, 182

MultiNet, 4, 5, 8, 18, 27, 40, 41, 51, 72,84, 115, 155

natural language interface, 6, 8, 36, 70natural language processing, 1, 35, 58,

61, 62, 67, 70, 85, 88, 113, 138,162, 166, 175, 182, 183

natural language understanding, 1, 2, 7negative, 33negative link, 147, 148, 161NEGRA corpus, 84, 167, 177NLI, see natural language interfaceNLP, see natural language processingNLU, see natural language understand-

ing

opening act, 35Osswald (2002), 18

PAIRUDIS, 3, 88, 91, 103, 104, 106–108, 110, 166, 170

Palomar et al. (2001), 137Pantel and Lin (2000), 107parse mode

chunk, 50, 52, 55, 124, 137sentence, 52, 124, 137text, 23, 49, 50

ParseTalk, 55part of speech, 35, 62, 67, 134, 173Partee (1972), 114partition element, 32, 112, 126, 129, 137,

146, 147, 157, 158Poesio (1996), 7Poesio (2000), 113, 129Poesio and Vieira (1998), 118Poesio et al. (1999), 132Pollard and Sag (1994), 48polysemy, 165

regular, 14PoS, see part of speechpositive, 33postcedent, 112, 118, 130PP attachment, see prepositional phrase

attachment

PP Attachment and Interpretation withRUles and DIsambiguation Statis-tics, see PAIRUDIS

PP disambiguation, see prepositional phrasedisambiguation

PP interpretation, see prepositional phraseinterpretation

PPD, see prepositional phrase disam-biguation

precision, 32premise, 58preposition

contracted, 132prepositional phrase attachment, 7, 30,

40, 59–61, 67–70, 75, 88, 93,96, 100, 105, 109, 110, 135, 166,175, 177

multiple, 108prepositional phrase disambiguation, 23,

48, 70, 84, 88–91, 96, 103, 105,109, 166, 168, 169, 171, 172

prepositional phrase interpretation, 40,69–72, 76, 88, 92, 96, 97, 105,110, 166, 175, 177

principle of parsimony, 109probability

empirical, 65pronoun

bound, 114expletive, 130paycheck, 114pleonastic, 130

Pustejovsky (1995), 14

Quillian (1968), 8

Ratnaparkhi (1998), 110Ratnaparkhi et al. (1994), 107, 110recall, 32recurrence

logical, 115reflexive verb

real, 132Reiners (1963), 45relation, 8

*an, 12, 46*auf, 12, 75, 76

202 Index

*bei, 12, 79*flp, 12, 81*opdiv, 76*opminus, 12, 83*ueber, 12, 72, 80*vor, 12, 82, 83aff, 47agt, 12, 25, 46, 47, 75ante, 12, 82anto, 12, 119, 120arg1, 46arg2, 46assoc, 12, 25, 52, 79attch, 12, 25, 47, 79attr, 12, 25, 80benf, 87caus, 12, 49chea, 84chsa, 84chsp2, 25, 47circ, 12, 47, 75, 79cstr, 47, 77, 82dircl, 12, 46, 72, 75, 80, 82dur, 12, 76equ, 12, 118exp, 46init, 77instr, 12, 80just, 12, 83loc, 12, 46, 72, 76, 79, 80, 83mannr, 178mcont, 12, 69, 81meth, 12, 81obj, 25, 46, 47origl, 12, 77origm, 12, 77ornt, 25, 47pars, 12, 77, 118pred, 12, 46, 47prop, 12, 25, 46, 47, 178purp, 87scar, 46sourc, 12, 78sub, 12, 24, 25, 46, 47subs, 12, 25, 46, 47syno, 12temp, 12, 25, 46, 47, 78, 81, 83

val, 12, 25, 80via, 70, 81

relative clause attachment, 76, 176response, 31right association principle, 100, 106, 108robustness, 51Rosenfeld and Huang (1992), 67rule activation

missing, 140spurious, 140

rule component, 75rule selection

incorrect, 140

Samuelsson (1996), 67Schulz (1998), 30Schulz and Helbig (1996), 48scope of quantifiers, 178semantic kernel, 36semantic molecule, see moleculesentence connective, 113signature, 9, 12, 90, 93SISD, see situation sense disambigua-

tionsister constituent, 71situation sense disambiguation, 172skip function, 64, 65, 96, 100, 154Small (1981), 54Small (1987), 54Small and Rieger (1982), 54Soon et al. (1999), 162Soon et al. (2001), 162sort, 8, 9, 13, 24, 84, 94, 98, 102span

of words, 24sparse data, 58, 59, 66, 95, 96, 103, 112,

157statistical component, 75Staudt (2002), 5, 31Stetina and Nagao (1997), 107Stevenson and Wilks (2001), 173stopping criterion, 65, 96, 99, 154Strube and Hahn (1999), 163subsequent-mention, 135synecdoche

generalizing, 162particularizing, 162

Index 203

systematic ambiguity, 86causal, 87contents, 86locative, 86, 133temporal, 87

systematic indeterminacy, see system-atic ambiguity

test set, 32Tetreault (2001), 163TiMBL, 31, 68, 106, 108, 173Tjaden (1996), 74train and test set approach, 32train set, 32type

ab, 13abs, 13, 84abs-geogr, 20abs-info, 20abs-object, 15, 17, 20abs-potag, 20abs-situation, 20acc, 6, 71action, 19ad, 13animal-object, 20animate-object, 14, 20aq, 13art-con-geogr, 20art-substance, 20as, 13, 84ass-quality, 19at, 13attribute, 20ax-mov-art-discrete, 20categ, 21, 24closed, 36co, 13, 18com-quality, 19completing, 36con, 21con-geogr, 20con-info, 20con-object, 15, 16, 18, 20, 120con-potag, 20d, 13, 24, 100da, 13, 98

dat, 71dempro, 148det, 21, 41, 147dn, 13, 98dy, 13dyn-abs-situation, 20dyn-situation, 19ent, 9, 13entity, 15, 19event, 19fe, 13fem, 148for-entity, 19fq, 13fquant, 21fun-quality, 19ge, 9, 21gen, 71gq, 13gr, 13graduator, 19haben, 28human-object, 14, 20, 120, 141, 143hypo, 9, 21ident, 129indefpro, 148indet, 21, 147institution, 14, 20, 141io, 13, 100ktype, 23l, 13l-sign, 27lay, 22lg, 13loc-sit-descriptor, 19m, 13masc, 148md, 13me, 13meas-quality, 19meas-unit, 19measurement, 19ment-action, 19ment-dyn-abs-situation, 20ment-event, 19ment-stat-abs-situation, 20ment-stat-situation, 19

204 Index

mo, 13mod-sit-descriptor, 19modality, 20mov-art-discrete, 20mov-nat-discrete, 20mov-nonanimate-con-potag, 20mq, 13mult, 21n, 117na, 13nat-con-geogr, 20nat-discrete, 20nat-substance, 20neut, 148nfquant, 21ng, 13nn, 13nolay, 21nonanimate-con-potag, 20nonax-mov-art-discrete, 20noninstit-abs-potag, 20nonmeas-quality, 19nonment-action, 19nonment-dyn-abs-situation, 20, 161nonment-event, 19nonment-stat-abs-situation, 20, 161nonment-stat-situation, 19nonmov-art-discrete, 20, 143nonmov-nat-discrete, 20nonmov-nonanimate-con-potag, 20nonnum-quantifier, 19nonoper-attribute, 20nonreal, 21nonthc-relation, 20nq, 13nu, 13num-quantifier, 19o, 13, 18, 94oa, 13object, 19, 31, 119one, 21open, 36oper-attribute, 20oq, 13ord-quality, 19osi-entity, 18, 19p, 13

perspro, 117, 148pl, 117, 119plant-object, 20possdet, 117, 148posspro, 148property, 19prot-abs-object, 20prot-con-object, 20prot-discrete, 20prot-method, 20prot-substance, 20prot-theor-concept, 20, 161proto, 21, 24qf, 13ql, 13qn, 13qual-graduator, 19quality, 19quan-graduator, 19quant, 147quantifier, 19quantity, 19re, 13real, 9, 21reflpro, 117, 148rel-quality, 19relation, 20rq, 13s, 13sd, 13sg, 117, 119si, 13, 18, 21, 84si-lay, 21sit-descriptor, 19situa, 21, 24situation, 15, 18, 19sp, 9, 21st, 13, 84, 98, 100stat-abs-situation, 20stat-situation, 19substance, 119t, 13, 74t’, 74ta, 13tem-abstractum, 20tem-sit-descriptor, 19thc-relation, 20

Index 205

tot-quality, 19tq, 13var, 21

van Deemter and Kibble (2000), 112van Rijsbergen (1975), 33variant, 14Vieira and Poesio (2000), 138Vilain et al. (1995), 158Volk (2000), 85Volk (2001), 107, 109

WCF, see word class functionWCF machine, 36WCFA, 4, 23, 35, 37, 50WCFM, see WCF machineWeiss and Kulikowski (1991), 32Wilks and Stevenson (1998), 173Winograd (1972), 70, 112WOCADI, 5, 23, 27, 30, 35, 40, 50, 72,

84, 117, 124, 134, 139, 143, 176WOrd ClAss based DIsambiguating parser,

see WOCADIword class function, 35word expert parsing, 54word sense disambiguation, 23, 49, 104,

109, 165, 166, 171, 172world knowledge, 70, 112, 114, 115, 161,

162, 172, 176, 178, 179WSD, see word sense disambiguationWu and Furugori (1996), 30WWW, 107, 166

Yeh (2000), 32Yeh and Vilain (1998), 109

Zavrel et al. (1997), 110