Presentation | 2007-05-25 Text Classification Method Based on Hierarchical Relations among Words Katsuji BESSHO, Toshio UCHIYAMA, Ryoji KATAOKA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Most of conventional text classification methods use symmetric measures such as cosine similarity or Euclidean metric of feature vectors derived from documents. This paper introduces to use Kullback-Leibler metric, which is non-symmetric, of the vectors for the text classification. We found that Kullback-Leibler metric instead of cosine similarity can reveal different aspect of association between words. Considering this property, this paper proposes the combined classification method which uses both cosine-Euclidean measure and KL metric. The experimental results showed that the proposed method improves classification accuracies of the method using only the cosine-Euclidean measure. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Concept Vector / Kullback-Leibler Metric / Text Classification |
Paper # | PRMU2007-15,MI2007-15 |
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Committee | PRMU |
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Conference Date | 2007/5/17(1days) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Text Classification Method Based on Hierarchical Relations among Words |
Sub Title (in English) | |
Keyword(1) | Concept Vector |
Keyword(2) | Kullback-Leibler Metric |
Keyword(3) | Text Classification |
1st Author's Name | Katsuji BESSHO |
1st Author's Affiliation | NTT Cyber Solutions Laboratories, NTT Corporation() |
2nd Author's Name | Toshio UCHIYAMA |
2nd Author's Affiliation | NTT Cyber Solutions Laboratories, NTT Corporation |
3rd Author's Name | Ryoji KATAOKA |
3rd Author's Affiliation | NTT Cyber Solutions Laboratories, NTT Corporation |
Date | 2007-05-25 |
Paper # | PRMU2007-15,MI2007-15 |
Volume (vol) | vol.107 |
Number (no) | 57 |
Page | pp.pp.- |
#Pages | 6 |
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