Bilingual Pronoun Resolution
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Bilingual Pronoun Resolution: Experiments in English and French Cat˘ alina˘ Barbu A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy 2003 This work or any part thereof has not been presented in any form to the University or to any other body whether for purposes of assessment, publication or for any other purpose (unless previously indicated). Save for any express acknowledgements, references and/or bibliographies cited in the work, I confirm that the intellectual content of the work is the result of my own efforts and of no other person. Abstract Anaphora resolution has been a subject of research in computational linguistics for more than 25 years. The interest it aroused was due to the importance that anaphoric phenomena play in the coherence and cohesiveness of natural language. A deep understanding of a text is impossible without knowledge about how individual concepts relate to each other; a shallow understanding of a text is often impeded without resolving anaphoric phenomena. In the larger context of anaphora resolution, pronoun resolution has benefited from a wide interest, with pronominal anaphora being one of the most frequent discourse phenomena. The problem has been approached in a variety of manners, in various languages. The research presented in this thesis approaches the problem of pronoun resolution in the context of multilingual NLP applications. In the global information society we are living in, fast access to information in the language of one’s choice is essential, and this access is facilitated by emerging multilingual NLP applications. As anaphora resolution is an integral part of many NLP applications, it necessarily has to be approached with the view of multilinguality in mind. The present project describes an original approach to bilingual anaphora resolution, for English and French. The method is based on combining hand crafted rules with automatic learning. Although only designed and tested for English and French, the approach has the potential to be extended to other languages. The steps preceding the practical development of the system consisted in an investigation of common and distinct features of English and French pronominal systems, while the evaluation of the system provided an interesting insight into specific problems relating to bilingual anaphora resolution. iii Acknowledgements I would like to thank Professor Ruslan Mitkov, my director of studies, for his continuos support and interest in my work that he has shown during my PhD studies. To all my colleagues in the Research Group in Computational Linguistics, a big Thank You! for making my whole PhD experience more enjoyable; also, for many helpful discussions that helped shaping my ideas. Special thanks go to Professor Dan Cristea, who first introduced me to the world of Computational Linguistics. I am also grateful to other people whose input contributed to this work. Many thanks to Monique Rolbert and Mike Jackson, my supervisors, for their constructive comments at various points during my research. Also to my examiners for their patience in reading my thesis and for their helpful suggestions. Many thanks to the University of Wolverhampton, who provided the financial support that made this work possible. Finally, my gratitude goes to my parents, Viorel and Margareta and to my husband, Rab, whose continuos encouragement helped me complete this thesis. iv Abbreviations AI - Artificial Intelligence API - Application Programming Interface AR - Anaphora Resolution BNC - British National Corpus CL - Computational Linguistics CT - Centering Theory GA - Genetic Algorithm GB - Government and Binding ML - Machine Learning MUC - Message Understanding Conference NLP - Natural Language Processing NLG - Natural Language Generation NP - Noun Phrase VP - Verb Phrase v Table of Contents List of Figures xv List of Tables xvii 1 Introduction 1 1.1 About anaphora . ........................ 2 1.2 Applications of anaphora resolution in NLP . ............. 4 1.3 Anaphora resolution in a multilingual environment . ...... 6 1.4 The task . ............................... 8 1.4.1 General overview ........................ 8 1.4.2 Pronouns tackled ........................ 10 1.4.3 Aims of the system . ................. 10 1.5 Terminology ............................... 12 1.6 Aims of the research . ........................ 13 1.7 Outline of the thesis . ........................ 15 2 Anaphoric expressions in English and French 19 2.1 Overview . ............................... 19 2.2 Pronominal systems . ........................ 21 2.2.1 Personal pronouns . ................. 21 vii 2.2.2 Possessive pronouns and determiners . ........... 26 2.2.3 Demonstratives ......................... 27 2.2.4 Reflexives . ......................... 30 2.2.5 French indefinite pronouns . .................. 31 2.2.6 Grammatical features of anaphoric pronouns . ....... 34 2.3 Non-anaphoric pronouns . .................. 36 2.3.1 Classes of non-anaphoric pronouns . ........... 37 2.3.2 French-specific non-anaphoric constructions . ....... 40 2.4 Conclusions . ......................... 41 3 Automatic anaphora resolution 43 3.1 Overview ................................ 43 3.2 Knowledge sources . ......................... 44 3.2.1 Types of knowledge sources .................. 44 3.2.2 Morpho-syntactic features . .................. 46 3.2.3 The role of semantics . .................. 49 3.2.4 Discourse constraints . .................. 51 3.2.5 When and where to apply? - the relative importance of factors 53 3.3 Pre-processing tools . ......................... 55 3.4 Anaphora resolution so far . .................. 58 3.4.1 Syntax-based methods . .................. 58 3.4.2 Knowledge-poor methods . .................. 59 3.4.3 Machine learning methods . .................. 61 3.4.4 Automatic resolution of other anaphoric types . ....... 63 3.4.5 Anaphora resolution for French . ........... 64 3.4.6 Multilingual anaphora resolution methods ........... 66 viii 3.5 Conclusions ............................... 67 4 Corpus annotation and analysis 69 4.1 Introduction ............................... 69 4.2 Anaphoric expressions across different text genres . ...... 70 4.3 Corpus annotation for anaphora resolution . ............. 71 4.3.1 General issues . ........................ 71 4.3.2 Annotation schemes . ................. 72 4.3.3 Annotation tools ........................ 75 4.4 Corpus annotation for ARMAL . ................. 76 4.4.1 Overview . ........................ 76 4.4.2 Choice of data . ........................ 77 4.4.3 Peculiarities of technical manuals . ............. 78 4.4.4 Corpus . ........................ 79 4.4.5 Data analysis . ........................ 80 4.5 Corpus annotation . ........................ 84 4.5.1 Overview . ........................ 84 4.5.2 Manual coreference annotation ................. 85 4.5.3 Automatic annotation . ................. 90 4.5.4 The DTD . ........................ 91 4.6 Conclusions ............................... 93 5 A system for bilingual anaphora resolution 95 5.1 Overview . ............................... 95 5.2 Machine learning for anaphora resolution . ............. 96 5.2.1 Brief outline of machine learning techniques . ...... 96 5.2.2 Anaphora resolution as a classification task . ...... 105 ix 5.3 A hybrid anaphora resolver . .................. 106 5.3.1 General description . .................. 106 5.3.2 Filtering rules . ......................... 107 5.3.3 Disjunction rules . .................. 109 5.3.4 Machine learning module . .................. 109 5.3.5 Training/learning features . .................. 111 5.3.6 Building the classifier . .................. 116 5.3.7 Applying the classifier . .................. 118 5.4 A genetic algorithm for anaphora resolution . ........... 119 5.4.1 Introduction . ......................... 119 5.4.2 System description . .................. 119 5.4.3 Operators . ......................... 123 5.5 The system . ......................... 125 5.5.1 Overview . ......................... 125 5.5.2 Text preparation . .................. 125 5.5.3 Language specific pre-processing . ........... 128 5.5.4 Language identification module . ........... 131 5.5.5 Anaphora resolution module .................. 132 5.5.6 Output . ......................... 134 5.5.7 Execution flow ......................... 135 5.5.8 Technical implementation details . ........... 136 5.6 Conclusions . ......................... 136 6 Evaluation 137 6.1 Overview ................................ 137 6.2 Evaluation issues in automatic anaphora resolution . ....... 138 x 6.2.1 Gold corpus . ........................ 138 6.2.2 Qualitative evaluation . ................. 140 6.2.3 Comparative evaluation . ................. 143 6.2.4 Evaluating machine learning methods ............. 145 6.2.5 Reliability of the evaluation . ................. 146 6.3 Evaluation methodology for ARMAL ................. 147 6.3.1 Aims of the evaluation . ................. 147 6.3.2 General issues . ........................ 147 6.3.3 Comparative evaluation . ................. 148 6.3.4 Qualitative evaluation . ................. 149 6.4 Results . ............................... 150 6.4.1 Filtering rules . ........................ 150 6.4.2 Learning module ........................ 151 6.4.3 Overall evaluation . ................. 153 6.4.4 Discussion . ........................ 155 6.4.5 Cross-genre evaluation . ................. 156 6.4.6 Comparative evaluation . ................. 157 6.4.7 Evaluation