Word Sense Disambiguation

Abstract

This paper describes a program that disambignates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories. Roget's categories serve as approximations of conceptual classes. The categories listed for a word in Roger's index tend to correspond to sense distinctions; thus selecting the most likely category provides a useful level of sense disambiguatiou. The selection of categories is accomplished by identifying and weighting words that are indicative of each category when seen in context, using a Bayesian theoretical framework. Other statistical approaches have required special corpora or hand-labeled training examples for much of the lexicon. Our use of class models overcomes this knowledge acquisition bottleneck, enabling training on unresUicted monolingual text without human intervention. Applied to the 10 million word Grolier's Encyclopedia, the system correctly disambiguated 92% of the instances of 12 polysemous words that have been previously studied in the literature. 1. Problem Formulation This paper presents an approach to word sense disambiguation that uses classes of words to derive models useful for disambignating individual words in context. "Sense" is not a well defined concept; it has been based on subjective and often subtle distinctions in topic, register, dialect, collocation, part of speech and valency. For the purposes of this study, we will define the senses of a word as the categories listed for that word in Roger's International Thesaurus (Fourth Edition Chapman, 1977). 1 Sense disambiguation will constitute 1. Note that this edition of Roger's Thesaurus is much more e0ttm$ive than the 1911 vm'sion, though somewhat more difficult to obtain in electronic form, One could me other other concept hlemrehics, such as WordNet (Miller, 1990) or the LDOCE mbject codes (Slator, 1991). All that it necessary is • set of semamic categories and • list of the words in each category. selecting the listed category which is most probable given the surrounding context. This may appear to be a particularly crude approximation, but as shown in the example below and in the table of results, it is surprisingly successful.