Short text classification based on strong feature thesaurus

Bing-kun WANG; Yong-feng HUANG; Wan-xia YANG; Xing LI

doi:10.1631/jzus.C1100373

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Short text classification based on strong feature thesaurus

计算机与自动化 | Updated：2022-12-09

- Short text classification based on strong feature thesaurus
- Short text classification based on strong feature thesaurus
- 信息与电子工程前沿（英文） 2012年13卷第9期页码：649-659
- Affiliations：
  
  1.Information Cognitive and Intelligent System Research Institute, Department of Electronic and Engineering, Tsinghua University, Beijing 100084, China
  2.Information Technology National Laboratory, Tsinghua University, Beijing 100084, China
- Author bio：
  
  [ "Bing-kun WANG, E-mail: Wangbingkun77@yahoo.com.cn; wbk10@mails.tsinghua.edu.cn" ]
- Funds：
  
  Project (No. 20111081023) supported by the Tsinghua University Initiative Scientific Research Program, China
- DOI：10.1631/jzus.C1100373
  中图分类号： TP391.4
- 收稿：2011-12-19，
  
  修回：2012-;8-;3，
  
  纸质出版：2012-09
- Accepted：
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Short text classification based on strong feature thesaurus[J]. 信息与电子工程前沿（英文）, 2012,13(9):649-659.

Bing-kun WANG, Yong-feng HUANG, Wan-xia YANG, et al. Short text classification based on strong feature thesaurus[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(9): 649-659.
Short text classification based on strong feature thesaurus[J]. 信息与电子工程前沿（英文）, 2012,13(9):649-659. DOI： 10.1631/jzus.C1100373.

Bing-kun WANG, Yong-feng HUANG, Wan-xia YANG, et al. Short text classification based on strong feature thesaurus[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(9): 649-659. DOI： 10.1631/jzus.C1100373.

摘要

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 Nave Bayes Multinomial.

关键词

Keywords

references

D.M. Blei , , , A.Y. Ng , , , M.I. Jordan . . Latent Dirichlet allocation . . J. Mach. Learn. Res. , , 2003 . . 3 ( ( 1 ): ): 993 - - 1022 . . . .

D. Bollegala , , , Y. Matsuo , , , M. Ishizuka . . Measuring Semantic Similarity Between Terms Using Web Search Engine . . Proc. 16th Int. Conf. on World Wide Web , , 2007 . . p.757 - - 766 . . DOI: 10.1145/1242572.1242675 http://doi.org/10.1145/1242572.1242675 . .

D. Bollegala , , , D. Weir , , , J. Carroll . . Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification . . Proc. 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies , , 2011 . . p.132 - - 141 . . . .

CAS (Chinese Academy of Sciences) . . Chinese Lexical Analysis System of the CAS . . Institute of Computing Technology, Chinese Academy of Sciences. Available from , , http://ictclas.org/ http://ictclas.org/ , , [Accessed on Sept. 20, 2011] 2010 . . .

E. Gabrilovich , , , S. Markovitch . . Feature Generation for Text Categorization Using World Knowledge . . Proc. 19th Int. Joint Conf. on Artificial Intelligence , , 2005 . . p.1048 - - 1053 . . . .

E. Gabrilovich , , , S. Markovitch . . Overcoming the Brittleness Bottleneck Using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge . . Proc. 21st National Conf. on Artificial Intelligence , , 2006 . . p.1301 - - 1306 . . . .

T.L. Griffiths , , , M. Steyvers . . Finding scientific topics . . PNAS , , 2004 . . 101 ( ( suppl_1 ): ): 5228 - - 5235 . . DOI: 10.1073/pnas.0307752101 http://doi.org/10.1073/pnas.0307752101 . .

G. Heinrich . . Parameter Estimation for Text Analysis . . . Technical Report, University of Leipzig, Germany. Available from , , http://www.arbylon.net/publications/text-est.pdf http://www.arbylon.net/publications/text-est.pdf , , 2005 . . .

X. Hu , , , N. Sun , , , C. Zhang , , , T.S. Chua . . Exploiting Internal and External Semantics for the Clustering of Short Texts Using World Knowledge . . Proc. 18th ACM Conf. on Information and Knowledge Management , , 2009 . . p.919 - - 928 . . DOI: 10.1145/1645953.1646071 http://doi.org/10.1145/1645953.1646071 . .

D. Koller , , , N. Friedman . . Probabilistic Graphical Models: Principles and Techniques , , MIT Press , , : : Cambridge, USA 2009 . . p.3 - - 6 . . . .

Y.H. Li , , , D. McLean , , , Z.A. Bandar , , , J.D. O'shea , , , K. Crockett . . Sentence similarity based on semantic nets and corpus statistics . . IEEE Trans. Knowl. Data Eng. , , 2006 . . 18 ( ( 8 ): ): 1138 - - 1150 . . DOI: 10.1109/TKDE.2006.130 http://doi.org/10.1109/TKDE.2006.130 . .

C.D. Manning , , , P. Raghavan , , , H. Schütze . . Introduction to Information Retrieval , , Cambridge University Press , , : : Cambridge, UK 2008 . . p.257 DOI: 10.1017/CBO9780511809071 http://doi.org/10.1017/CBO9780511809071 . .

D. Metzler , , , S. Dumais , , , C. Meek . . Similarity Measures for Short Segments of Text . . 29th European Conf. in Information Retrieval Research , , 2007 . . p.16 - - 27 . . . .

T. Peng , , , W.L. Zuo , , , F.L. He . . SVM based adaptive learning method for text classification from positive and unlabeled documents . . Knowl. Inf. Syst. , , 2008 . . 16 ( ( 3 ): ): 281 - - 301 . . DOI: 10.1007/s10115-007-0107-1 http://doi.org/10.1007/s10115-007-0107-1 . .

X.H. Phan , , , L.M. Nguyen , , , S. Horiguchi . . Learning to Classify Short and Sparse Text & Web with Hidden Topics from Large-Scale Data Collections . . Proc. 17th Int. Conf. on World Wide Web , , 2008 . . p.91 - - 100 . . DOI: 10.1145/1367497.1367510 http://doi.org/10.1145/1367497.1367510 . .

X.H. Phan , , , C.T. Nguyen , , , T. Le D. , , , L.M. Nguyen , , , S. Horiguchi , , , Q.T. Ha . . A hidden topic-based framework toward building applications with short Web documents . . IEEE Trans. Knowl. Data Eng. , , 2011 . . 23 ( ( 7 ): ): 961 - - 976 . . DOI: 10.1109/TKDE.2010.27 http://doi.org/10.1109/TKDE.2010.27 . .

X.J. Quan , , , G. Liu , , , Z. Lu , , , X.L. Ni , , , W.Y. Liu . . Short text similarity based on probabilistic topics . . Knowl. Inf. Syst. , , 2010 . . 25 ( ( 3 ): ): 473 - - 491 . . DOI: 10.1007/s10115-009-0250-y http://doi.org/10.1007/s10115-009-0250-y . .

M. Sahami , , , T.D. Heilman . . A Web-Based Kernel Function for Measuring the Similarity of Short Text Snippets . . Proc. 15th Int. Conf. on World Wide Web , , 2006 . . p.377 - - 386 . . DOI: 10.1145/1135777.1135834 http://doi.org/10.1145/1135777.1135834 . .

Sohu Research & Development Center . . Text Classification Corpus of Sogou Labs . . Available from , , http://www.sogou.com/labs/dl/c.html http://www.sogou.com/labs/dl/c.html , , [Accessed on Sept. 20, 2011] 2008 . . .

I.H. Witten , , , E. Frank , , , M.A. Hall . . Data Mining: Practical Machine Learning Tools and Techniques , , Morgan Kaufmann Press , , : : San Francisco, USA 2011 . . .

W.T. Yih , , , C. Meek . . Improving Similarity Measures for Short Segments of Text . . Proc. 22nd National Conf. on Artificial Intelligence , , 2007 . . p.1489 - - 1494 . . . .

Y.T. Zhang , , , L. Gong , , , Y.C. Wang . . An improved TF-IDF approach for text classification . . J. Zhejiang Univ.-Sci. , , 2005 . . 6A ( ( 1 ): ): 49 - - 55 . . DOI: 10.1631/jzus.2005.A0049 http://doi.org/10.1631/jzus.2005.A0049 . .

C.Q. Zong . . Statistical Signal Processing , , Tsinghua University Press , , : : Beijing, China 2008 . . p.344 . .

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