SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text

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This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambiguate all content words in a text using the sense from WordNet. SenseLearner participated in the SENSEVAL-3 English all words task, and achieved an average accuracy of 64.6%.

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4 p.

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Mihalcea, Rada, 1974- & Faruque, Ehsanul 2004.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 178 times . More information about this paper can be viewed below.

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This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambiguate all content words in a text using the sense from WordNet. SenseLearner participated in the SENSEVAL-3 English all words task, and achieved an average accuracy of 64.6%.

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4 p.

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  • Association for Computational Linguistics (ACL)/SIGLEX Senseval-3 Conference, 2004, Barcelona, Spain

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UNT Scholarly Works

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  • 2004

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  • Jan. 31, 2011, 2:01 p.m.

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  • March 27, 2014, 1:05 p.m.

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Mihalcea, Rada, 1974- & Faruque, Ehsanul. SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text, paper, 2004; [Stroudsburg, Pennsylvania]. (digital.library.unt.edu/ark:/67531/metadc30961/: accessed October 15, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.