Capturing salience with a trainable cache model for zero-anaphora resolution

Authors:
Ryu Iida;Kentaro Inui;Yuji Matsumoto
Affiliations:
Tokyo Institute of Technology, Ôokayama, Meguro, Tokyo, Japan;Nara Institute of Science and Technology, Takayama, Ikoma, Nara, Japan;Nara Institute of Science and Technology, Takayama, Ikoma, Nara, Japan
Venue:
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Year:
2009

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Abstract

This paper explores how to apply the notion of caching introduced by Walker (1996) to the task of zero-anaphora resolution. We propose a machine learning-based implementation of a cache model to reduce the computational cost of identifying an antecedent. Our empirical evaluation with Japanese newspaper articles shows that the number of candidate antecedents for each zero-pronoun can be dramatically reduced while preserving the accuracy of resolving it.