A WordNet-based Algorithm for Word Sense Disambiguation

Abstract

We present an algorithm for automatic word sense disambiguation, based on lexical knowledge contained in WordNet and on the results of surface-syntactic analysis. The algorithm is part of a system that analyzes texts in order to acquire knowledge in the presence of as little pre-coded semantic knowledge as possible. On the other hand, we want to make the best use of public-domain information sources such as WordN et . Rather than depend on large amounts of hand-crafted knowledge or statistical data from large corpora, we use syntactic information and information in WordNet and minimize the need for other knowledge sources in the word sense disambiguation process. We propose to guide disambiguation by semantic similarity between words and heuristic rules based on this similarity. The algorithm has been applied to the Canadian Income Tax Guide. Test results indicate that even on a relatively small text the proposed method produces correct noun meaning more than 72% of the time.