Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach

Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach Hwee Tou Ng Defence Science Organisation 20 Science Park Drive Singapore 118230 nhweet ou©trantor, dso. gov. sg Hian Beng Lee Defence Science Organisation 20 Science Park Drive Singapore 118230 lhianben@trant or. dso. gov. sg Abstract In this paper, we present a new approach for word sense disambiguation (WSD) us- ing an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neigh- boring words, morphological form, the un- ordered set of surrounding words, local collocations, and verb-object syntactic re- lation. We tested our WSD program, named LEXAS, on both a common data set used in previous work, as well as on a large sense-tagged corpus that we sep- arately constructed. LEXAS achieves a higher accuracy on the common data set, and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of WoRDNET. 1 Introduction One important problem of Natural Language Pro- cessing (NLP) is figuring out what a word means when it is used in a particular context. The different meanings of a word are listed as its various senses in a dictionary. The task of Word Sense Disambigua- tion (WSD) is to identify the correct sense of a word in context. Improvement in the accuracy of iden- tifying the correct word sense will result in better machine translation systems, information retrieval systems, etc. For example, in machine translation, knowing the correct word sense helps to select the appropriate target words to use in order to translate into a target language. In this paper, we present a new approach for WSD using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech (POS) of neighboring words, morphologi- cal form, the unordered set of surrounding words, local collocations, and verb-object syntactic rela- tion. To evaluate our WSD program, named LEXAS (LEXical Ambiguity-resolving _System), we tested it on a common data set involving the noun "interest" used by Bruce and Wiebe (Bruce and Wiebe, 1994). LEXAS achieves a mean accuracy of 87.4% on this data set, which is higher than the accuracy of 78% reported in (Bruce and Wiebe, 1994). Moreover, to test the scalability of LEXAS, we have acquired a corpus in which 192,800 word occurrences have been manually tagged with senses from WORD- NET, which is a public domain lexical database con- taining about 95,000 word forms and 70,000 lexical concepts (Miller, 1990). These sense tagged word occurrences consist of 191 most frequently occur- ring and most ambiguous nouns and verbs. When tested on this large data set, LEXAS performs better than the default strategy of picking the most fre- quent sense. To our knowledge, this is the first time that a WSD program has been tested on such a large scale, and yielding results better than the most fre- quent heuristic on highly ambiguous …

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