Abstract

A novel unsupervised genetic word sense disambiguation (GWSD) algorithm is proposed in this paper. The algorithm first uses WordNet to determine all possible senses for a set of words, then a genetic algorithm is used to maximize the overall semantic similarity on this set of words. A novel conceptual similarity function combining domain information is also proposed to compute similarity between senses in WordNet. GWSD is tested on two sets of domain terms and obtains good results. A weighted genetic word sense disambiguation (WGWSD) algorithm is then proposed to disambiguate words in a general corpus. Experiments on SemCor are carried out to compare WGWSD with previous work.

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