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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (9): 649-659    DOI: 10.1631/jzus.C1100373
    
Short text classification based on strong feature thesaurus
Bing-kun Wang, Yong-feng Huang, Wan-xia Yang, Xing Li
Information Cognitive and Intelligent System Research Institute, Department of Electronic and Engineering, Tsinghua University, Beijing 100084, China; Information Technology National Laboratory, Tsinghua University, Beijing 100084, China
Short text classification based on strong feature thesaurus
Bing-kun Wang, Yong-feng Huang, Wan-xia Yang, Xing Li
Information Cognitive and Intelligent System Research Institute, Department of Electronic and Engineering, Tsinghua University, Beijing 100084, China; Information Technology National Laboratory, Tsinghua University, Beijing 100084, China
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摘要： Data sparseness, the evident characteristic of short text, has always been regarded as the main cause of the low accuracy in the classification of short texts using statistical methods. Intensive research has been conducted in this area during the past decade. However, most researchers failed to notice that ignoring the semantic importance of certain feature terms might also contribute to low classification accuracy. In this paper we present a new method to tackle the problem by building a strong feature thesaurus (SFT) based on latent Dirichlet allocation (LDA) and information gain (IG) models. By giving larger weights to feature terms in SFT, the classification accuracy can be improved. Specifically, our method appeared to be more effective with more detailed classification. Experiments in two short text datasets demonstrate that our approach achieved improvement compared with the state-of-the-art methods including support vector machine (SVM) and Na?ve Bayes Multinomial.
关键词： Short text;  Classification;  Data sparseness;  Semantic;  Strong feature thesaurus (SFT);  Latent Dirichlet allocation (LDA)    
Abstract: Data sparseness, the evident characteristic of short text, has always been regarded as the main cause of the low accuracy in the classification of short texts using statistical methods. Intensive research has been conducted in this area during the past decade. However, most researchers failed to notice that ignoring the semantic importance of certain feature terms might also contribute to low classification accuracy. In this paper we present a new method to tackle the problem by building a strong feature thesaurus (SFT) based on latent Dirichlet allocation (LDA) and information gain (IG) models. By giving larger weights to feature terms in SFT, the classification accuracy can be improved. Specifically, our method appeared to be more effective with more detailed classification. Experiments in two short text datasets demonstrate that our approach achieved improvement compared with the state-of-the-art methods including support vector machine (SVM) and Na?ve Bayes Multinomial.
Key words: Short text    Classification    Data sparseness    Semantic    Strong feature thesaurus (SFT)    Latent Dirichlet allocation (LDA)
收稿日期: 2011-12-19 出版日期: 2012-09-05
CLC:  TP391.4  
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Bing-kun Wang
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引用本文:

Bing-kun Wang, Yong-feng Huang, Wan-xia Yang, Xing Li. Short text classification based on strong feature thesaurus. Front. Inform. Technol. Electron. Eng., 2012, 13(9): 649-659.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1100373    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I9/649

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