Authors
Shimon Edelman
Cornell University
Abstract
We describe a method for automatic word sense disambiguation using a text corpus and a machine- readable dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous word listed in the MRD. A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. Experiments show that this method performs well, and can learn even from very sparse training data.
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References found in this work BETA

Word and Object.Willard Van Orman Quine - 1960 - Les Etudes Philosophiques 17 (2):278-279.
Word and Object.Henry W. Johnstone - 1961 - Philosophy and Phenomenological Research 22 (1):115-116.
Lexical Semantics.D. A. Cruse - 1986 - Cambridge University Press.
On Semantics.Uriel Weinreich - 1980 - University of Pennsylvania Press.

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