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.
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Keyword(in English) neural networks / naive Bayes / posterior probability / characteristic words
Paper # NC2003-95
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Committee NC
Conference Date 2003/12/1(1days)
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Registration To Neurocomputing (NC)
Language JPN
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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
Date of Issue