Presentation	2002/9/12 Speeding Up Support Vector Machines for Natural Language Processing Hideki ISOZAKI, Hideto KAZAWA,
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Abstract(in English)	The Support Vector Machine (SVM) is a new powerful learning algorithm. It has been used for Pattern Recognition such as character recognition. Now, it is also used in the Natural Language Processing (NLP) community, and SVM-based methods are proposed for various NLP tasks such as document classification, part-of-speech tagging, chunking, named entity recognition, term recognition, dependency analysis, and parsing. These SVM-based methods are comparable to or better than conventional methods. In some tasks, however, SVM-based systems run orders-of-magnitude slower than conventional methods. Here, we report an efficient algorithm that exploits characteristics of NLP data.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Support Vector Machine / Natural Language Processing / Named Entity Recognition
Paper #	WIT2002-16
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Conference Information
Committee	WIT
Conference Date	2002/9/12(1days)
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Paper Information
Registration To	Well-being Information Technology(WIT)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Speeding Up Support Vector Machines for Natural Language Processing
Sub Title (in English)
Keyword(1)	Support Vector Machine
Keyword(2)	Natural Language Processing
Keyword(3)	Named Entity Recognition
1st Author's Name	Hideki ISOZAKI
1st Author's Affiliation	NTT Communication Science Laboratories()
2nd Author's Name	Hideto KAZAWA
2nd Author's Affiliation	NTT Communication Science Laboratories
Date	2002/9/12
Paper #	WIT2002-16
Volume (vol)	vol.102
Number (no)	319
Page	pp.pp.-
#Pages	6
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