Reducing Lexical Semantic Complexity with Systematic Polysemous Classes and Underspecification

Paul Buitelaar

In: Workshop on Syntactic and Semantic Complexity in Natural Language Processing Systems (ANLP-NAACL'00), April 29 - May 3. Applied Natural Language Processing Conference (ANLP-00), April 29 - May 3, Seattle, WA, USA, Pages 14-19, Association for Computational Linguistics, Morristown, NJ, USA, 2000.


This paper presents an algorithm for finding systematic polysemous classes in WordNet and similar semantic databases, based on a definition in (Apresjan 1973). The introduction of systematic polysemous classes can reduce the amount of lexical semantic processing, because the number of disambiguation decisions can be restricted more clearly to those cases that involve real ambiguity (homonymy). In many applications, for instance in document categorization, information retrieval, and information extraction, it may be sufficient to know if a given word belongs to a certain class (underspecified sense) rather than to know which of its (related) senses exactly to pick. The approach for finding systematic polysemous classes is based on that of (Buitelaar 1998a, Buitelaar 1998b), while addressing some previous shortcomings.

Buitelaar_2000_RLS.pdf (pdf, 34 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence