David Yarowsky
ABSTRACT
This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints---that words tend to have one sense per discourse and one sense per collocation---exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96%.
- Baum, L. E., "An Inequality and Associated Maximization Technique in Statistical Estimation of Probabilistic Functions of a Markov Process," Inequalities, v 3, pp 1--8, 1972.Google Scholar
- Black, Ezra, "An Experiment in Computational Discrimination of English Word Senses," in IBM Journal of Research and Development, v 232, pp 185--194, 1988. Google ScholarDigital Library
- Brill, Eric, "A Corpus-Based Approach to Language Learning," Ph.D. Thesis, University of Pennsylvania, 1993. Google ScholarDigital Library
- Brown, Peter, Stephen Della Pietra, Vincent Della Pietra, and Robert Mercer, "Word Sense Disambiguation using Statistical Methods," Proceedings of the 29th Annual Meeting of the Association for Computational Linguistics, pp 264--270, 1991. Google ScholarDigital Library
- Bruce, Rebecca and Janyce Wiebe, "Word-Sense Disambiguation Using Decomposable Models," in Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, NM, 1994. Google ScholarDigital Library
- Church, K. W., "A Stochastic Parts Program an Noun Phrase Parser for Unrestricted Text," in Proceeding, IEEE International Conference on Acoustics, Speech and Signal Processing, Glasgow, 1989.Google Scholar
- Dagan, Ido and Alon Itai, "Word Sense Disambiguation Using a Second Language Monolingual Corpus", Computational Linguistics, v 20, pp 563--596, 1994. Google ScholarDigital Library
- Dempster, A. P., Laird, N. M., and Rubin, D. B., "Maximum Likelihood From Incomplete Data via the EM Algorithm," Journal of the Royal Statistical Society, v 39, pp 1--38, 1977.Google Scholar
- Gale, W., K. Church, and D. Yarowsky, "A Method for Disambiguating Word Senses in a Large Corpus," Computers and the Humanities, 26, pp 415--439, 1992.Google ScholarCross Ref
- Gale, W., K. Church, and D. Yarowsky. "Discrimination Decisions for 100,000-Dimensional Spaces." In A. Zampoli, N. Calzolari and M. Palmer (eds.), Current Issues in Computational Linguistics: In Honour of Don Walker, Kluwer Academic Publishers, pp. 429--450, 1994.Google Scholar
- Guthrie, J., L. Guthrie, Y. Wilks and H. Aidinejad, "Subject Dependent Co-occurrence and Word Sense Disambiguation," in Proceedings of the 29th Annual Meeting of the Association for Computational Linguistics, pp 146--152, 1991. Google ScholarDigital Library
- Hearst, Marti, "Noun Homograph Disambiguation Using Local Context in Large Text Corpora," in Using Corpora, University of Waterloo, Ontario, 1991.Google Scholar
- Leacock, Claudia, Geoffrey Towell and Ellen Voorhees "Corpus-Based Statistical Sense Resolution," in Proceedings, ARPA Human Language Technology Workshop, 1993. Google ScholarDigital Library
- Lehman, Jill Fain, "Toward the Essential Nature of Statistical Knowledge in Sense Resolution", in Proceedings of the Twelfth National Conference on Artificial Intelligence, pp 734--471, 1994. Google ScholarDigital Library
- Lesk, Michael, "Automatic Sense Disambiguation: How to tell a Pine Cone from an Ice Cream Cone," Proceeding of the 1986 SIGDOC Conference, Association for Computing Machinery, New York, 1986. Google ScholarDigital Library
- Miller, George, "WordNet: An On-Line Lexical Database," International Journal of Lexicography, 3, 4, 1990.Google ScholarCross Ref
- Mosteller, Frederick, and David Wallace, Inference and Disputed Authorship: The Federalist, Addison-Wesley, Reading, Massachusetts, 1964.Google Scholar
- Rivest, R. L., "Learning Decision Lists," in Machine Learning, 2, pp 229--246, 1987. Google ScholarDigital Library
- Schütze, Hinrich, "Dimensions of Meaning," in Proceedings of Supercomputing '92, 1992. Google ScholarDigital Library
- Slator, Brian, "Using Context for Sense Preference," in Text-Based Intelligent Systems: Current Research in Text Analysis, Information Extraction and Retrieval, P. S. Jacobs, ed., GE Research and Development Center, Schenectady, New York, 1990.Google Scholar
- Veronis, Jean and Nancy Ide, "Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries," in Proceedings, COLING-90, pp 389--394, 1990. Google ScholarDigital Library
- Yarowsky, David "Word-Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora," in Proceedings, COLING-92, Nantes, France, 1992. Google ScholarDigital Library
- Yarowsky, David, "One Sense Per Collocation," in Proceedings, ARPA Human Language Technology Workshop, Princeton, 1993. Google ScholarDigital Library
- Yarowsky, David, "Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French," in Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, NM, 1994. Google ScholarDigital Library
- Yarowsky, David. "Homograph Disambiguation in Speech Synthesis." In J. Hirschberg, R. Sproat and J. van Santen (eds.), Progress in Speech Synthesis, Springer-Verlag, to appear.Google Scholar
- Unsupervised word sense disambiguation rivaling supervised methods
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