Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity

Most previous corpus-based algorithms disambiguate a word with a classifier trained from previous usages of the same word. Separate classifiers have to be trained for different words. We present an algorithm that uses the same knowledge sources to disambiguate different words. The algori thm does not require a sense-tagged corpus and exploits the fact that two different words are likely to have similar meanings if they occur in identical local contexts. 1 I n t r o d u c t i o n Given a word, its context and its possible meanings, the problem of word sense disambiguation (WSD) is to determine the meaning of the word in that context. WSD is useful in many natural language tasks, such as choosing the correct word in machine translation and coreference resolution. In several recent proposals (Hearst, 1991; Bruce and Wiebe, 1994; Leacock, Towwell, and Voorhees, 1996; Ng and Lee, 1996; Yarowsky, 1992; Yarowsky, 1994), statistical and machine learning techniques were used to extract classifiers from hand-tagged corpus. Yarowsky (Yarowsky, 1995) proposed an unsupervised method that used heuristics to obtain seed classifications and expanded the results to the other parts of the corpus, thus avoided the need to hand-annotate any examples. Most previous corpus-based WSD algorithms determine the meanings of polysemous words by exploiting their local con tex t s . A basic intuition that underlies those algorithms is the following: (i) Two occurrences of the same word have identical meanings if they have similar local contexts. In other words, most previous corpus-based WSD algorithms learn to disambiguate a polysemous word from previous usages of the same word. This has several undesirable consequences. Firstly, a word must occur thousands of times before a good classifier can be learned. In Yarowsky's experiment (Yarowsky, 1995), an average of 3936 examples were used to disambiguate between two senses. In Ng and Lee's experiment, 192,800 occurrences of 191 words were used as training examples. There are thousands of polysemous words, e.g., there are 11,562 polysemous nouns in WordNet. For every polysemous word to occur thousands of times each, the corpus must contain billions of words. Secondly, learning to disambiguate a word from the previous usages of the same word means that whatever was learned for one word is not used on other words, which obviously missed generality in natural languages. Thirdly, these algorithms cannot deal with words for which classifiers have not been learned. In this paper, we present a WSD algorithm that relies on a different intuition: (2) Two different words are likely to have similar meanings if they occur in identical local contexts. Consider the sentence: (3) The new facility will employ 500 of the existing 600 employees The word "facility" has 5 possible meanings in WordNet 1.5 (Miller, 1990): (a) installation, (b) proficiency/technique, (c) adeptness, (d) readiness, (e) toilet /bathroom. To disambiguate the word, we consider other words that appeared in an identical local context as "facility" in (3). Table 1 is a list of words that have also been used as the subject of "employ" in a 25-million-word Wall Street Journal corpus. The "freq" column are the number of times these words were used as the subject of "employ".