Abstract
This paper presents a suite of methods and results for the semantic disambiguation of WordNet glosses. WordNet is a resource widely used in natural language processing and artificial intelligence. Intended and designed as a lexical database, WordNet exhibits some deficiencies when used as a knowledge base. By semantically disambiguating the words in the glosses, we add pointers from each word to its concept or synset, and this increases the connectivity between the WordNet concepts by approximately an order of magnitude. We show how lexical chains and other applications can be built on this richly connected WordNet. The semantic disambiguation of the WordNet glosses is performed using automatic methods based on a set of heuristics. The precision of the semantic annotation is improved by using voting between the disambiguation system described here and another WSD system. The entire WordNet 2.0 has been disambiguated with an overall precision of 86% and is available at http://xwn.hlt.utdallas.edu.
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