Anaphora resolution by antecedent identification followed by anaphoricity determination

ACM Transactions on Asian Language Information Processing

Volumn 4, Issue 4, 2005, Pages 417-434

  Iida, Ryu ^a   Inui, Kentaro ^a   Matsumoto, Yuji ^a  

^a NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

Anaphora resolution; Anaphoricity determination; Antecedent identification

Indexed keywords

ANAPHORA RESOLUTION; ANAPHORICITY DETERMINATION; ANTECEDENT IDENTIFICATION;

CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; MATHEMATICAL MODELS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 33745177326     PISSN: 15300226     EISSN: 15300226     Source Type: Journal    
DOI: 10.1145/1113308.1113312     Document Type: Review

References (25)

1. BALDWIN, B. 1995. Cogniac: A discourse processing engine. Ph.D. thesis, Department of Computer and Information Sciences, University of Pennsylvania, Philadelphia, PA.
2. BEAN, D. L. AND RILOFF, E. 1999. Corpus-based identification of non-anaphoric noun phrases. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL). 373-380.
3. BRENNAN, S. E., FRIEDMAN, M. W., AND POLLARD, C. 1987. A centering approach to pronouns. In Proceedings of the 25th Annual Meeting of the Association for Computational Linguistics (ACL). 155-162.
4. GE, N., HALE, J., AND CHARNIAK, E. 1998. A statistical approach to anaphora resolution. In Proceedings of the 6th Workshop on Very Large Corpora. 161-170.
5. GROSZ, B. J., JOSHI, A. K., AND WEINSTEIN, S. 1995. Centering: A framework for modeling the local coherence of discourse. Computational Linguistics 21, 2, 203-226.
6. IIDA, R., INUI, K., TAKAMURA, H., AND MATSUMOTO, Y. 2003. Incorporating contextual cues in trainable models for coreference resolution. In Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics (EACL) Workshop on The Computational Treatment of Anaphora. 23-30.
7. KUDO, T. AND MATSUMOTO, Y. 2002. Japanese dependency analysis using cascaded chunking. In Proceedings of the 6th Conference on Natural Language Learning 2002 (CoNLL). 63-69.
8. LAPPIN, S. AND LEASS, H. J. 1994. An algorithm for pronominal anaphora resolution. Computational Linguistics, 20, 4, 535-561.
9. MATSUMOTO, Y., KITAUCHI, A., YAMASHITA, T., HIRANO, Y., MATSUDA, H., TAKAOKA, K., AND ASAHARA, M. 2000. Morphological Analysis System ChaSen version 2.2.1 Manual.
10. MCCALLUM, A. AND WELLNER, B. 2003. Object consolidation by graph partitioning with a conditionally trained distance metric. In Proceedings of the KDD-2003 Workshop on Data Cleaning, Record Linkage, and Object Consolidation. 19-24.
11. MCCARTHY, J. F. AND LEHNERT, W. G. 1995. Using decision trees for coreference resolution. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI). 1050-1055.
12. MITKOV, R. 1997. Factors in anaphora resolution: they are not the only things that matter, a case study based on two different approaches. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics (ACL) and the 8th Conference of the European Chapter of the Association for Computational Linguistics (EACL) Workshop on Operational Factors in Practical, Robust Anaphora Resolution.
13. NAKAIWA, H. AND SHIRAI, S. 1996. Anaphora resolution of Japanese zero pronouns with deictic reference. In Proceedings of the 16th International Conference on Computational Linguistics (COLING). 812-817.
14. NATURAL LANGUAGE RESEARCH INSTITUTE. 1964. Burui Goi Hyo (in Japanese). Shuuei Publishing.
15. NG, V. 2004. Learning noun phrase anaphoricity to improve coreference resolution: Issues in representation and optimization. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL). 152-159.
16. NG, V. AND CARDIE, C. 2002a. Improving machine learning approaches to coreference resolution. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL). 104-111.
17. NG, V. AND CARDIE, C. 2002b. Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution. In Proceedings of the 19th International Conference on Computational Linguistics (COLING). 730-736.
18. NG, V. AND CARDIE, C. 2002c. Combining sample selection and error-driven pruning for machine learning of coreference rules. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP). 55-62.
19. OKUMURA, M. AND TAMURA, K. 1996. Zero pronoun resolution in Japanese discourse based on centering theory. In Proceedings of the 16th International Conference on Computational Linguistics (COLING). 871-876.
20. POESIO, M., URYUPINA, O., VIEIRA, R., ALEXANDROV-KABADJOV, M., AND GOULART, R. 2004. Discourse-new detectors for definite description resolution: A survey and a preliminary proposal. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL) Workshop on Reference Resolution and its Applications. 47-54.
21. SOON, W. M., NG, H. T., AND LIM, D. C. Y. 2001. A machine learning approach to coreference resolution of noun phrases. Computational Linguistics, 27, 4, 521-544.
22. STRUBE, M. AND MÜLLER, C. 2003. A machine learning approach to pronoun resolution in spoken dialogue. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL). 168-175.
23. URYUPINA, O. 2003. High-precision identification of discourse new and unique noun phrases. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL) Student Research Workshop. 80-86.
24. VAPNIK, V. N. 1998. Statistical Learning Theory. Adaptive and Learning Systems for Signal Processing Communications, and control. Wiley, New York.
25. YANG, X., ZHOU, G., SU, J., AND TAN, C. L. 2003. Coreference resolution using competition learning approach. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL). 176-183.