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 Scholar
Digital Library
- Brill, Eric, "A Corpus-Based Approach to Language Learning," Ph.D. Thesis, University of Pennsylvania, 1993. Google Scholar
Digital 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 Scholar
Digital 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 Scholar
Digital 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 Scholar
Digital 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 Scholar
Cross 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 Scholar
Digital 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 Scholar
Digital 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 Scholar
Digital 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 Scholar
Digital Library
- Miller, George, "WordNet: An On-Line Lexical Database," International Journal of Lexicography, 3, 4, 1990.Google Scholar
Cross 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 Scholar
Digital Library
- Schütze, Hinrich, "Dimensions of Meaning," in Proceedings of Supercomputing '92, 1992. Google Scholar
Digital 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 Scholar
Digital 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 Scholar
Digital Library
- Yarowsky, David, "One Sense Per Collocation," in Proceedings, ARPA Human Language Technology Workshop, Princeton, 1993. Google Scholar
Digital 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 Scholar
Digital 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
Index Terms
(auto-classified)Unsupervised word sense disambiguation rivaling supervised methods
Recommendations
An unsupervised method for word sense disambiguation
AbstractWord sense disambiguation (WSD) finds the actual meaning of a word according to its context. This paper presents a novel WSD method to find the correct sense of a word present in a sentence. The proposed method uses both the WordNet ...
Unsupervised Word-Sense Disambiguation Using Bilingual Comparable Corpora
An unsupervised method for word-sense disambiguation using bilingual comparable corpora was developed. First, it extracts word associations, i.e., statistically significant pairs of associated words, from the corpus of each language. Then, it aligns ...
Unsupervised word sense disambiguation using bilingual comparable corpora
COLING '02: Proceedings of the 19th international conference on Computational linguistics - Volume 1An unsupervised method for word sense disambiguation using a bilingual comparable corpus was developed. First, it extracts statistically significant pairs of related words from the corpus of each language. Then, aligning pairs of related words ...





Comments