Abstract
In this paper we present a noun phrase coreference resolution system which aims to enhance the identification of the coreference realized by string matching. For this purpose, we make two extensions to the standard learn-ing-based resolution framework. First, to improve the recall rate, we introduce an additional set of features to capture the different matching patterns between noun phrases. Second, to improve the precision, we modify the instance selection strategy to allow non-anaphors to be included during training instance generation. The evaluation done on MEDLINE data set shows that the combination of the two extensions provides significant gains in the F-measure.
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Aone, C., Bennett, S.W.: Evaluating automated and manual acquistion of anaphora resolution strategies. In: Proceedings of the 33rd Annual Meeting of the Association for Compuational Linguistics, pp. 122–129 (1995)
McCarthy, J., Lehnert, Q.: Using decision trees for coreference resolution. In: Proceedings of the 14th International Conference on Artificial Intelligences, pp. 1050–1055 (1995)
Soon, W., Ng, H., Lim, D.: A machine learning approach to coreference resolution of noun phrases. Computational Linguistics 27, 521–544 (2001)
Ng, V., Cardie, C.: Improving machine learning approaches to coreference resolution. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, pp. 104–111 (2002)
Yang, X., Zhou, G., Su, J., Tan, C.: Coreference resolution using competition learning approach. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, Japan (2003)
MUC-6: Proceedings of the Sixth Message Understanding Conference. Morgan Kaufmann Publishers, San Francisco, CA (1995)
MUC-7: Proceedings of the Seventh Message Understanding Conference. Morgan Kaufmann Publishers, San Francisco, CA (1998)
Poesio, M., Vieira, R.: A corpus-based investigation of definite description use. Computational Linguistics 24, 183–261 (1998)
Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string distance metrics for namematching tasks. In: Procedings of IJCAI 2003 Workshop on Information Integration on the Web (2003)
Vieira, R., Poesio, M.: An empirically based system for processing definite descriptions. Computational Linguistics 27, 539–592 (2001)
Strube, M., Rapp, S., Muller, C.: The influence of minimum edit distance on reference resolution. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Philadelphia, pp. 312–319 (2002)
Castano, J., Zhang, J., Pustejovsky, J.: Anaphora resolution in biomedical literature. In: International Symposium on Reference Resolution, Alicante, Spain (2002)
Zhou, G., Su, J.: Error-driven HMM-based chunk tagger with context-dependent lexicon. In: Proceedings of the Joint Conference on Empirical Methods on Natural Language Processing and Very Large Corpus, Hong Kong (2000)
Zhou, G., Su, J.: Named Entity recognition using a HMM-based chunk tagger. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia (2002)
Shen, D., Zhang, J., Zhou, G., Su, J., Tan, C.: Effective adaptation of hidden markov modelbased named-entity recognizer for biomedical domain. In: Proceedings of ACL 2003 Workshop on Natural Language Processing in Biomedicine, Japan (2003)
Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann Publishers, San Francisco (1993)
Vilain, M., Burger, J., Aberdeen, J., Connolly, D., Hirschman, L.: A model-theoretic coreference scoring scheme. In: Proceedings of the Sixth Message understanding Conference (MUC-6), pp. 45–52. Morgan Kaufmann, San Francisco (1995)
Ng, V., Cardie, C.: Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution. In: Proceedings of the 19th International Conference on Computational Linguistics, COLING 2002 (2002)
McCallum, A., Wellner, B.: Toward conditional models of identity uncertainty with application to proper noun coreference. In: Procedings of IJCAI 2003 Workshop on Information Integration on the Web, pp. 79–86 (2003)
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Yang, X., Zhou, G., Su, J., Tan, C.L. (2005). Improving Noun Phrase Coreference Resolution by Matching Strings. In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_3
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DOI: https://doi.org/10.1007/978-3-540-30211-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24475-2
Online ISBN: 978-3-540-30211-7
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