SenseLearner: Word Sense Disambiguation for All Words in Unrestricted Text

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This paper describes SenseLearner, a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using WordNet senses.

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4 p.

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Mihalcea, Rada, 1974- & Csomai, Andras June 2005.

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This paper describes SenseLearner, a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using WordNet senses.

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4 p.

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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. The authors 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.

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  • Forty-Third Annual Meeting of the Association for Computational Linguistics (ACL), 2005, Ann Arbor, Michigan, United States

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  • June 2005

Added to The UNT Digital Library

  • Jan. 31, 2011, 2:01 p.m.

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  • March 27, 2014, 1:06 p.m.

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Mihalcea, Rada, 1974- & Csomai, Andras. SenseLearner: Word Sense Disambiguation for All Words in Unrestricted Text, paper, June 2005; [Stroudsburg, Pennsylvania]. (digital.library.unt.edu/ark:/67531/metadc30975/: accessed October 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.