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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 algorithm 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. | Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity Dekang Lin Department of Computer Science University of Manitoba Winnipeg Manitoba Canada R3T 2N2 lindek@cs.umanitoba.ca Abstract 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 algorithm 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 Introduction 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 contexts. A basic intuition that underlies those algorithms is the following 1 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 .
