An EMM-based Approach for Text Classification | Zendy

Jizhong Liang | Zendy; Xiao-Hu Zhou | Zendy; Peng Liu | Zendy; Linjie Guo | Zendy; Shaojie Bai | Zendy

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An EMM-based Approach for Text Classification

Author(s) -

Jizhong Liang,

Xiao-Hu Zhou,

Peng Liu,

Linjie Guo,

Shaojie Bai

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.05.065

Subject(s) - computer science , hidden markov model , support vector machine , artificial intelligence , context (archaeology) , machine learning , markov chain , sequence (biology) , natural language processing , paleontology , genetics , biology

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

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