A Method for Word Sense Disambiguation of Unrestricted Text

A Method for Word Sense Disambiguation of Unrestricted Text Rada Mihalcea and Dan I. Moldovan Department of Computer Science and Engineering Southern Methodist University Dallas, Texas, 75275-0122 (rada,moldovan}@seas.smu.edu Abstract Selecting the most appropriate sense for an am- biguous word in a sentence is a central prob- lem in Natural Language Processing. In this paper, we present a method that attempts to disambiguate all the nouns, verbs, adverbs and adjectives in a text, using the senses pro- vided in WordNet. The senses are ranked us- ing two sources of information: (1) the Inter- net for gathering statistics for word-word co- occurrences and (2)WordNet for measuring the semantic density for a pair of words. We report an average accuracy of 80% for the first ranked sense, and 91% for the first two ranked senses. Extensions of this method for larger windows of more than two words are considered. 1 Introduction Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing. Its solution impacts other tasks such as discourse, reference resolution, coherence, inference and others. WSD methods can be broadly classified into three types: 1. WSD that make use of the information provided by machine readable dictionaries (Cowie et al., 1992), (Miller et al., 1994), (Agirre and Rigau, 1995), (Li et al., 1995), (McRoy, 1992); 2. WSD that use information gathered from training on a corpus that has already been semantically disambiguated (super- vised training methods) (Gale et al., 1992), (Ng and Lee, 1996); 3. WSD that use information gathered from raw corpora (unsupervised training meth- ods) (Yarowsky, 1995) (Resnik, 1997). There are also hybrid methods that combine several sources of knowledge such as lexicon in- formation, heuristics, collocations and others (McRoy, 1992) (Bruce and Wiebe, 1994) (Ng and Lee, 1996) (Rigau et al., 1997). Statistical methods produce high accuracy re- sults for small number of preselected words. A lack of widely available semantically tagged cor- pora almost excludes supervised learning meth- ods. A possible solution for automatic acqui- sition of sense tagged corpora has been pre- sented in (Mihalcea and Moldovan, 1999), but the corpora acquired with this method has not been yet tested for statistical disambiguation of words. On the other hand, the disambiguation using unsupervised methods has the disadvan- tage that the senses are not well defined. None of the statistical methods disambiguate adjec- tives or adverbs so far. In this paper, we introduce a method that at- tempts to disambiguate all the nouns, verbs, ad- jectives and adverbs in a text, using the senses provided in WordNet (Fellbaum, 1998). To our knowledge, there is only one other method, recently reported, that disambiguates unre- stricted words in texts (Stetina et al., 1998). 2 A word-word dependency approach The method presented here takes advantage of the sentence context. The words are paired and an attempt is made to disambiguate one word within the context of the other word. This is done by searching on Internet with queries formed using different senses of one word, while keeping the other word …

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