An algorithm for pronominal anaphora resolution
Computational Linguistics
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A corpus-based investigation of definite description use
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HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
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CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
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NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Comparison of classification and ranking approaches to pronominal anaphora resolution in Czech
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Automatic evaluation of linguistic quality in multi-document summarization
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Antelogue: pronoun resolution for text and dialogue
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations
A pronoun anaphora resolution system based on factorial hidden Markov models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Narrowing the modeling gap: a cluster-ranking approach to coreference resolution
Journal of Artificial Intelligence Research
Journal of Biomedical Informatics
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EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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DAARC'11 Proceedings of the 8th international conference on Anaphora Processing and Applications
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HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
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Towards unsupervised learning of temporal relations between events
Journal of Artificial Intelligence Research
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Proceedings of the 22nd international conference on World Wide Web companion
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We present an algorithm for pronoun-anaphora (in English) that uses Expectation Maximization (EM) to learn virtually all of its parameters in an unsupervised fashion. While EM frequently fails to find good models for the tasks to which it is set, in this case it works quite well. We have compared it to several systems available on the web (all we have found so far). Our program significantly outperforms all of them. The algorithm is fast and robust, and has been made publically available for downloading.