A WordNet-based Algorithm for Word Sense Disambiguation

We present an algorithm for automatic word sense disambiguation ba.sed on lexical knowl edge contained in WordNet and on the results of surface-syntactic analvsis The algorithm lb part of a system that analyzes texts in or der to acquire knowledge in the presence of as little pre-coded semantic knowledge as possi blc On the other hand, we want lo make Hit besl us* of public-domain information sources such as WordNet 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 knowledg< sources in the word sense disambiguation process We propose to guide disambiguation by semantic similarity between words and heuris-tic rules based on this similarity I lu algorithm has been applied to the (anadian Income fax Guide Test results indicate that even on a rela-tivelv small text the proposed method produces correct noun meaning more than 11% of the tim< 1 Introduction This work is part of the project that amih at a svner gistic integration of Machine Learning and Natural Language Processing The long-t< rm goal of the project is a system that performs machine learning on the results of text analysis to acquire a useful collection of production rules Because such a svstem should not require extensive domain knowledge up front, text analysis is to be dont in a knowledge-scant setting and with mm imal user involvement A domain-independent surface-syntactic parser produces an analysis of a text fragment (usually a sentence) that undergoes interactive semantic 'Tins work was done while the first author was with the Knowledge Acquisition and Machine Learning group at the University of Ottawa The authors are gralcful to the Nat ural Sciences and Engineering Research Council of Canada far having supported this work with a Strategic Grant No STR0117764 interpretation By design we only need the user to approve the system s findings or prompt it for alternatives Also hy design we limit ourselves to information source* in the public domain inexpensive dictionaries and other lexical sources such as WordNet WordNel [Mill* r, 1990 Beckwith et al , 1991] is a very rich source of lexical knowledge Since most entries have multiple senses, we fare a severe problem of ambiguity The motivation for the work described here is the desire to design a word sense disambiguation (WSD) algorithm that satisfies the needs of our project …