Presentation | 2003/12/1 Extracting Characteristic Words of Text Using Neural Networks Kazumi SAITO, Ryohei NAKANO, |
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Abstract(in English) | In this paper, we discuss methods for detecting an adequate topic of documents and extracting characteristic words of such topics, by using two types of neural networks formalized as statistical models. The main features of these models are that their learning algorithms utilize an objective function that maximizes posterior probabilities for topic detection, and that characteristic words are extracted based on the magnitude of resulting parameter values. Through the experiments using a set of real Web pages, we evaluate the methods in the aspect of topic detection performances and extraction capabilities of characteristic words. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | neural networks / naive Bayes / posterior probability / characteristic words |
Paper # | NC2003-95 |
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Committee | NC |
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Conference Date | 2003/12/1(1days) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Extracting Characteristic Words of Text Using Neural Networks |
Sub Title (in English) | |
Keyword(1) | neural networks |
Keyword(2) | naive Bayes |
Keyword(3) | posterior probability |
Keyword(4) | characteristic words |
1st Author's Name | Kazumi SAITO |
1st Author's Affiliation | NTT Communication Science Laboratories() |
2nd Author's Name | Ryohei NAKANO |
2nd Author's Affiliation | Nagoya Institute of Technology |
Date | 2003/12/1 |
Paper # | NC2003-95 |
Volume (vol) | vol.103 |
Number (no) | 490 |
Page | pp.pp.- |
#Pages | 6 |
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