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ABSTRACT

In this paper, we view coreference resolution as a problem of ranking candidate partitions generated by different coreference systems. We propose a set of partition-based features to learn a ranking model for distinguishing good and bad partitions. Our approach compares favorably to two state-of-the-art coreference systems when evaluated on three standard coreference data sets.

REFERENCES

• 1: Chinatsu Aone , Scott William Bennett, Evaluating automated and manual acquisition of anaphora resolution strategies, Proceedings of the 33rd annual meeting on Association for Computational Linguistics, p.122-129, June 26-30, 1995, Cambridge, Massachusetts [doi>10.3115/981658.981675]
• 2: Amit Bagga , Breck Baldwin, Entity-based cross-document coreferencing using the Vector Space Model, Proceedings of the 36th annual meeting on Association for Computational Linguistics, August 10-14, 1998, Montreal, Quebec, Canada
• 3: Nikhil Bansal , Avrim Blum , Shuchi Chawla, Correlation Clustering, Proceedings of the 43rd Symposium on Foundations of Computer Science, p.238, November 16-19, 2002
• 4: D. Bean and E. Riloff. 2004. Unsupervised learning of contextual role knowledge for coreference resolution. In Proc. of HLT/NAACL, pages 297--304.
• 5: Adam L. Berger , Vincent J. Della Pietra , Stephen A. Della Pietra, A maximum entropy approach to natural language processing, Computational Linguistics, v.22 n.1, p.39-71, March 1996
• 6: C. Cardie and K. Wagstaff. 1999. Noun phrase coreference as clustering. In Proc. of EMNLP/VLC, pages 82--89.
• 7: W. Cohen. 1995. Fast effective rule induction. In Proc. of ICML, pages 115--123.
• 8: Michael Collins, Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms, Proceedings of the ACL-02 conference on Empirical methods in natural language processing, p.1-8, July 06, 2002 [doi>10.3115/1118693.1118694]
• 9: Sanda M. Harabagiu , Rǎzvan C. Bunescu , Steven J. Maiorano, Text and knowledge mining for coreference resolution, Second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies 2001, p.1-8, June 01-07, 2001, Pittsburgh, Pennsylvania [doi>10.3115/1073336.1073344]
• 10: R. Iida, K. Inui, H. Takamura, and Y. Matsumoto. 2003. Incorporating contextual cues in trainable models for coreference resolution. In Proc. of the EACL Workshop on The Computational Treatment of Anaphora.
• 11: Thorsten Joachims, Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775067]
• 12: A. Kehler. 1997. Probabilistic coreference in information extraction. In Proc. of EMNLP, pages 163--173.
• 13: Xiaoqiang Luo , Abe Ittycheriah , Hongyan Jing , Nanda Kambhatla , Salim Roukos, A mention-synchronous coreference resolution algorithm based on the Bell tree, Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p.135-es, July 21-26, 2004, Barcelona, Spain [doi>10.3115/1218955.1218973]
• 14: A. McCallum and B. Wellner. 2003. Toward conditional models of identity uncertainty with application to proper noun coreference. In Proc. of the IJCAI Workshop on Information Integration on the Web.
• 15: J. McCarthy and W. Lehnert. 1995. Using decision trees for coreference resolution. In Proc. of the IJCAI, pages 1050--1055.
• 16: Thomas S. Morton, Coreference for NLP applications, Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, p.173-180, October 03-06, 2000, Hong Kong [doi>10.3115/1075218.1075241]
• 17: C. Müller, S. Rapp, and M. Strube. 2002. Applying cotraining to reference resolution. In Proc. of the ACL, pages 352--359.
• 18: Vincent Ng , Claire Cardie, Combining sample selection and error-driven pruning for machine learning of coreference rules, Proceedings of the ACL-02 conference on Empirical methods in natural language processing, p.55-62, July 06, 2002 [doi>10.3115/1118693.1118701]
• 19: Vincent Ng , Claire Cardie, Improving machine learning approaches to coreference resolution, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, July 07-12, 2002, Philadelphia, Pennsylvania [doi>10.3115/1073083.1073102]
• 20: J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
• 21: Wee Meng Soon , Hwee Tou Ng , Daniel Chung Yong Lim, A machine learning approach to coreference resolution of noun phrases, Computational Linguistics, v.27 n.4, December 2001
• 22: Michael Strube , Christoph Müller, A machine learning approach to pronoun resolution in spoken dialogue, Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, p.168-175, July 07-12, 2003, Sapporo, Japan [doi>10.3115/1075096.1075118]
• 23: Michael Strube , Stefan Rapp , Christoph Müller, The influence of minimum edit distance on reference resolution, Proceedings of the ACL-02 conference on Empirical methods in natural language processing, p.312-319, July 06, 2002 [doi>10.3115/1118693.1118733]
• 24: Marc Vilain , John Burger , John Aberdeen , Dennis Connolly , Lynette Hirschman, A model-theoretic coreference scoring scheme, Proceedings of the 6th conference on Message understanding, November 06-08, 1995, Columbia, Maryland [doi>10.3115/1072399.1072405]
• 25: Xiaofeng Yang , Guodong Zhou , Jian Su , Chew Lim Tan, Coreference resolution using competition learning approach, Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, p.176-183, July 07-12, 2003, Sapporo, Japan [doi>10.3115/1075096.1075119]
• 26: D. Zelenko, C. Aone, and J. Tibbetts. 2004. Coreference resolution for information extraction. In Proc. of the ACL Workshop on Reference Resolution and its Applications, pages 9--16.