An unsupervised method for word-sense disambiguation using bilingual comparable corpora was developed. First, it extracts word associations, i.e., statistically significant pairs of associated words, from the corpus of each language. Then, it aligns word associations by consulting a bilingual dictionary and calculates correlation between senses of a target polysemous word and its associated words, which can be regarded as clues for identifying the sense of the target word. To overcome the problem of disparity of topical coverage between corpora of the two languages as well as the problem of ambiguity in word-association alignment, an algorithm for iteratively calculating a sense-vs.-clue correlation matrix for each target word was devised. Word-sense disambiguation for each instance of the target word is done by selecting the sense that maximizes the score, i.e., a weighted sum of the correlations between each sense and clues appearing in the context of the instance. An experiment using Wall Street Journal and Nihon Keizai Shimbun corpora together with the EDR bilingual dictionary showed that the new method has promising performance; namely, the F-measure of its sense selection was 74.6% compared to a baseline of 62.8%. The developed method will possibly be extended into a fully unsupervised method that features automatic division and definition of word senses.

This paper presents a new method for resolving lexical (word sense) ambiguities inherent in natural language sentences. The Sentence Analyzer (SENA) was developed to resolve such ambiguities by using constraints and example-based preferences. The ambiguities are packed into a single dependency structure, and grammatical and lexical constraints are applied to it in order to reduce the degree of ambiguity. The application of constraints is realized by a very effective constraint-satisfaction technique. Remaining ambiguities are resolved by the use of preferences calculated from an example-base, which is a set of fully parsed word-to-word dependencies acquired semi-automatically from on-line dictionaries.