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 Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Wikipedia / Semantic Relatedness / Support Vector Machine / Support Vector Regression |
Paper # | DE2010-26 |
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Committee | DE |
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Conference Date | 2010/11/29(1days) |
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Registration To | Data Engineering (DE) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
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 |
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