Presentation 2010-12-06
Semantic Relatedness Measurement Leveraging Various Features of Wikipedia and Identifying Influential Features
MASAHIRO ITO, KOTARO NAKAYAMA, TAKAHIRO HARA, SHOJIRO NISHIO,
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Abstract(in English) Recently, several semantic relatedness measures based on Wikipedia have been proposed. Each method, however, focuses on only a single feature such as the link structure, the category structure or link co-occurrences. This research identifies influential features for semantic relatedness within various features and proposes an efficient semantic relatedness measurement method that leverages multiple features of Wikipedia using machine learning.
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Keyword(in English) Wikipedia / Semantic Relatedness / Support Vector Machine / Support Vector Regression
Paper # DE2010-26
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Committee DE
Conference Date 2010/11/29(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Semantic Relatedness Measurement Leveraging Various Features of Wikipedia and Identifying Influential Features
Sub Title (in English)
Keyword(1) Wikipedia
Keyword(2) Semantic Relatedness
Keyword(3) Support Vector Machine
Keyword(4) Support Vector Regression
1st Author's Name MASAHIRO ITO
1st Author's Affiliation Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University()
2nd Author's Name KOTARO NAKAYAMA
2nd Author's Affiliation Center for Knowledge Structuring, The University of Tokyo
3rd Author's Name TAKAHIRO HARA
3rd Author's Affiliation Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
4th Author's Name SHOJIRO NISHIO
4th Author's Affiliation Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
Date 2010-12-06
Paper # DE2010-26
Volume (vol) vol.110
Number (no) 328
Page pp.pp.-
#Pages 6
Date of Issue