Abstract
Word sense disambiguation has been recognized as a major problem in natural language processing research for over forty years. Both quantitive and qualitative methods have been tried, but much of this work has been stymied by difficulties in acquiring appropriate lexical resources. The availability of this testing and training material has enabled us to develop quantitative disambiguation methods that achieve 92% accuracy in discriminating between two very distinct senses of a noun. In the training phase, we collect a number of instances of each sense of the polysemous noun. Then in the testing phase, we are given a new instance of the noun, and are asked to assign the instance to one of the senses. We attempt to answer this question by comparing the context of the unknown instance with contexts of known instances using a Bayesian argument that has been applied successfully in related tasks such as author identification and information retrieval. The proposed method is probably most appropriate for those aspects of sense disambiguation that are closest to the information retrieval task. In particular, the proposed method was designed to disambiguate senses that are usually associated with different topics.
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William Gale is in a statistics department at AT&T Bell Laboratories. He has done research in physics, radio astronomy, and economics in the past, and founded the Society for Artificial Intelligence and Statistics. His current interests include lexical issues such as word sense discrimination, word similarity measures, and word correspondences in parallel texts.
Kenneth Ward Church received his Ph.D. in Computer Science from MIT, and then went to work at AT&T Bell Laboratories on problems in speech and language. Recently, he has been advocating the use of statistical methods for analyzing large corpora.
David Yarowsky is currently pursuing a Ph.D. in Computer Science at the University of Pennsylvania. He spent several years at AT&T Bell Laboratories doing research in statistical natural language processing.
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Gale, W.A., Church, K.W. & Yarowsky, D. A method for disambiguating word senses in a large corpus. Comput Hum 26, 415–439 (1992). https://doi.org/10.1007/BF00136984
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DOI: https://doi.org/10.1007/BF00136984