Classifying Chinese texts in two steps

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This paper proposes a two-step method for Chinese text categorization (TC). In the first step, a Naive Bayesian classifier is used to fix the fuzzy area between two categories, and, in the second step, the classifier with more subtle and powerful features is used to deal with documents in the fuzzy area, which are thought of being unreliable in the first step. The preliminary experiment validated the soundness of this method. Then, the method is extended from two-class TC to multi-class TC. In this two-step framework, we try to further improve the classifier by taking the dependences among features into consideration in the second step, resulting in a Causality Naive Bayesian Classifier.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2005
Language
English
Article Type
Article; Proceedings Paper
Citation

NATURAL LANGUAGE PROCESSING - IJCNLP 2005, PROCEEDINGS BOOK SERIES: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, v.3651, pp.302 - 313

ISSN
0302-9743
URI
http://hdl.handle.net/10203/92533
Appears in Collection
CS-Journal Papers(저널논문)
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