An EMM-based Approach for Text Classification

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• 作者：J.G. Liang^a ; ^{liangjiguang@iie.ac.cn} ; X.F. Zhou^a ; P. Liu^a ; L. Guo^a ; S. Bai^a ; ^b
• 关键词：Type your keywords here ; separated by semicolons
• 刊名：Procedia Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：17
• 期：Complete
• 页码：506-513
• 全文大小：308 K

文摘

In this paper, a classification method named explicit Markov model is applied for text classification. Currently some machine learning technologies, such as support vector machine (SVM), have been discussed widely in text classification. However, these methods consider that any two features are independent and ignore the language structure information. Hidden Markov model is a powerful tool for sequence tagging problems. This paper presents a new method called explicit Markov model (EMM) which is based on HMM for text classification. EMM make better use of the context information between the observation symbols. Our experiments are conducted on three datasets: Reuter's 21578 R8 dataset, WebKB and Fudan University Chinese text classification corpus. Experimental results show that the performance of EMM is comparable to SVM for text classification.