Semi-supervised text categorization by active search

In automated text categorization, given a small number of labeled documents, it is very challenging, if not impossible, to build a reliable classifier that is able to achieve high classification accuracy. To address this problem, a novel webassisted text categorization framework is proposed in this paper. Important keywords are first automatically identified from the available labeled documents to form the queries. Search engines are then utilized to retrieve from the Web a multitude of relevant documents, which are then exploited by a semi-supervised framework. To our best knowledge, this work is the first study of this kind. Extensive experimental study shows the encouraging results of the proposed text categorization framework: using Google as the web search engine, the proposed framework is able to reduce the classification error by 30% when compared with the stateof-the-art supervised text categorization method. Categories and Subject Descriptors H.3.3 [Information Systems]: Info...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Zenglin Xu, Rong Jin, Kaizhu Huang, Michael R. Lyu, Irwin King
• Keywords: CIKM 2008, Information Management, Search Engine, Text Categorization, Text Categorization Framework