A Machine Learning Approach to Preference Strategies for Anaphor Resolution

Chapters in this book

1. Prelim pages i
2. Table of contents v
3. Editors’ Foreword vii
4. I. Computational Treatment
5. A Sequenced Model of Anaphora and Ellipsis Resolution 3
6. How to Deal with Wicked Anaphora? 17
7. A Machine Learning Approach to Preference Strategies for Anaphor Resolution 47
8. Decomposing Discourse 73
9. A Lightweight Approach to Coreference Resolution for Named Entities in Text 97
10. A Unified Treatment of Spanish se 113
11. II. Theretical, Psycholinguistic and Cognitive Issues
12. Binding and Beyond 139
13. Modelling Referential Choice in Discourse 163
14. Degrees of Indirectness 199
15. Pronominal Interpretation and the Syntax-Discourse Interface 221
16. Top-down and Bottom-up Effects on the Interpretation of Weak Object Pronouns in Greek 239
17. Different Forms Have Different Referential Properties 261
18. Referential Accessibility and Anaphor Resolution 283
19. III. Corpus-Based Studies
20. The Predicate-Argument Structure of Discourse Connectives 303
21. Combining Centering-Based Models of Salience and Information Structure for Resolving Intersentential Pronominal Anaphora 329
22. Pronouns Without NP Antecedents 351
23. Syntactic Form and Discourse Accessibility 365
24. Coreference and Anaphoric Relations of Demonstrative Noun Phrases in Multilingual Corpus 385
25. Anaphoric Demonstratives 403
26. Focu, Activation, and This -Noun Phrases 429