SenseLearner

descriptionPublicationkeyboard_double_arrow_right Article , Conference object , Report 01 Jan 2005 United States Publisher:Association for Computational Linguistics (ACL)Journal:Proceedings of the ACL 2005 on Interactive poster and demonstration sessions - ACL '05

Authors: Mihalcea, Rada; Csomai, Andras;

doi: 10.3115/1225753.1225767

SenseLearner

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Abstract

This paper describes SENSELEARNER --- a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using WordNet senses. We evaluate the accuracy of SENSELEARNER on several standard sense-annotated data sets, and show that it compares favorably with the best results reported during the recent SENSEVAL evaluations.

Country

United States

Related Organizations

University of North Texas
United States

Keywords

word sense disambiguation, SenseLearner, sense annotated data sets, WordNet senses

1 Research products, page 1 of 1

Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger
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