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| Research on Automatic Text Classification Based on a Hybrid Language Model |
| Zheng De-quan; Li Sheng; Zhao Tie-jun; Yu Hao |
| MOE-MS Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, Harbin 150001, China |
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Abstract With the volume of information available on the Internet and corporate intranets continues to increase, text classification has become one of the key technology in organizing and processing large amount of document data. This paper gives a novel method of Chinese text categorization based on a combination of ontology with statistical method. In this study, first, linguistic ontology knowledge bank will be respectively acquired by learning training corpus for various classes to determine the various categorizations. For a actual document, the evaluation value will respectively be gotten by various linguistic ontology knowledge bank and the categorization will be judged by the highest evaluation value. This method is compared with Bayes, k-nearest neighbor and support vector machine, The primary experimental results show that the method outperforms that previous work.
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Received: 17 August 2005
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2007, Vol. 29 
