Using multiple knowledge sources for word sense discrimination
Computational Linguistics
Using WordNet to disambiguate word senses for text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
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SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Information access and retrieval with semantic background knowledge
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SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Extracting semantic hierarchies from a large on-line dictionary
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
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ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
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COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
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COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
NetSerf: using semantic knowledge to find Internet information archives
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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This paper describes a heuristic-based approach to word-sense disambiguation. The heuristics that are applied to disambiguate a word depend on its part of speech, and on its relationship to neighboring salient words in the text. Parts of speech are found through a tagger, and related neighboring words are identified by a phrase extractor operating on the tagged text. To suggest possible senses, each heuristic draws on semantic relations extracted from a Webster's dictionary and the semantic thesaurus WordNet. For a given word, all applicable heuristics are tried, and those senses that are rejected by all heuristics are discarded. In all, the disambiguator uses 39 heuristics based on 12 relationships.