Full Text:   <6275>

CLC number: TP391.4

On-line Access: 2012-09-05

Received: 2011-12-19

Revision Accepted: 2012-06-25

Crosschecked: 2012-08-03

Cited: 13

Clicked: 8631

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.9 P.649-659


Short text classification based on strong feature thesaurus

Author(s):  Bing-kun Wang, Yong-feng Huang, Wan-xia Yang, Xing Li

Affiliation(s):  Information Cognitive and Intelligent System Research Institute, Department of Electronic and Engineering, Tsinghua University, Beijing 100084, China; more

Corresponding email(s):   Wangbingkun77@yahoo.com.cn, wbk10@mails.tsinghua.edu.cn

Key Words:  Short text, Classification, Data sparseness, Semantic, Strong feature thesaurus (SFT), Latent Dirichlet allocation (LDA)

Bing-kun Wang, Yong-feng Huang, Wan-xia Yang, Xing Li. Short text classification based on strong feature thesaurus[J]. Journal of Zhejiang University Science C, 2012, 13(9): 649-659.

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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100373

T1 - Short text classification based on strong feature thesaurus
A1 - Bing-kun Wang
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A1 - Wan-xia Yang
A1 - Xing Li
J0 - Journal of Zhejiang University Science C
VL - 13
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1100373

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 Naïve Bayes Multinomial.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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