Presentation 1997/7/25
Mistake-driven learning with thesaurus for text categorization
Takefumi Yamazaki, Ido Dagan,
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Abstract(in English) This paper extends the mistake-driven learner WINNOW, which has been highly studied in the theoretical machine learning literature, to better utilize thesauri for text categorization. In our method not only words but also semantic categories given by the thesaurus are used as features in a classifier. New filtering and disambiguation methods are used as pre-processing to solve the problems caused by the use of the thesaurus. In the experiment we test RWCP corpus and verify our method.
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Paper # NLC97-21
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Committee NLC
Conference Date 1997/7/25(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Mistake-driven learning with thesaurus for text categorization
Sub Title (in English)
1st Author's Name Takefumi Yamazaki
1st Author's Affiliation NTT Comunication Science Laboratories()
2nd Author's Name Ido Dagan
2nd Author's Affiliation Bar Ilan University
Date 1997/7/25
Paper # NLC97-21
Volume (vol) vol.97
Number (no) 200
Page pp.pp.-
#Pages 8
Date of Issue