Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Computational Linguistics - Special issue on web as corpus
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Estimation of stochastic attribute-value grammars using an informative sample
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Web-based models for natural language processing
ACM Transactions on Speech and Language Processing (TSLP)
Japanese zero pronoun resolution based on ranking rules and machine learning
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Chasing the ghost: recovering empty categories in the Chinese treebank
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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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.