SemTag and seeker: bootstrapping the semantic web via automated semantic annotation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Four methods for supervised word sense disambiguation
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
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The need for robust and easily extensible systems for word sense disambiguation coupled with successes in training systems for a variety of tasks using large on-line corpora has led to extensive research into corpus-based statistical approaches to this problem. Promising results have been achieved by vector space representations of context, clustering combined with a semantic knowledge base, and decision lists based on collocational relations. We evaluate these techniques with respect to three important criteria: how their definition of context affects their ability to incorporate different types of disambiguating information, how they define similarity among senses, and how easily they can generalize to new senses. The strengths and weaknesses of these systems provide guidance for future systems which must capture and model a variety of disambiguating information, both syntactic and semantic.