Zero-anaphora resolution by learning rich syntactic pattern features

Authors:
Ryu Iida;Kentaro Inui;Yuji Matsumoto
Affiliations:
Nara Institute of Science and Technology;Nara Institute of Science and Technology;Nara Institute of Science and Technology
Venue:
ACM Transactions on Asian Language Information Processing (TALIP)
Year:
2007

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Abstract

We approach the zero-anaphora resolution problem by decomposing it into intrasentential and intersentential zero-anaphora resolution tasks. For the former task, syntactic patterns of zeropronouns and their antecedents are useful clues. Taking Japanese as a target language, we empirically demonstrate that incorporating rich syntactic pattern features in a state-of-the-art learning-based anaphora resolution model dramatically improves the accuracy of intrasentential zero-anaphora, which consequently improves the overall performance of zero-anaphora resolution.