Publication IEICE TRANSACTIONS on Information and SystemsVol.E90-DNo.7pp.1092-1102 Publication Date: 2007/07/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.7.1092 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Natural Language Processing Keyword: zero-anaphora resolution, Web-based features, maximum entropy, classifier,
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Summary: In this paper, we propose a learning classifier based on maximum entropy (ME) for resolving zero-anaphora in Chinese text. Besides regular grammatical, lexical, positional and semantic features motivated by previous research on anaphora resolution, we develop two innovative Web-based features for extracting additional semantic information from the Web. The values of the two features can be obtained easily by querying the Web using some patterns. Our study shows that our machine learning approach is able to achieve an accuracy comparable to that of state-of-the-art systems. The Web as a knowledge source can be incorporated effectively into the ME learning framework and significantly improves the performance of our approach.