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| Junping Zhu|
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|Co-authors||Qiang Qiu, Qu W., YanChun Zhang|
|Authorship||Publications (1), datasets (0), tools (0)|
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|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Building a text classifier by a keyword and Wikipedia knowledge||Keyword
|Lecture Notes in Computer Science||English||2009||Traditional approach for building text classifiers usually require a lot of labeled documents, which are expensive to obtain. In this paper, we propose a new text classification approach based on a keyword and Wikipedia knowledge, so as to avoid labeling documents manually. Firstly, we retrieve a set of related documents about the keyword from Wikipedia. And then, with the help of related Wikipedia pages, more positive documents are extracted from the unlabeled documents. Finally, we train a text classifier with these positive documents and unlabeled documents. The experiment result on 20Newsgroup dataset show that the proposed approach performs very competitively compared with NB-SVM, a PU learner, and NB, a supervised learner.||0||0|