[1] Yang Sichun, Gao Chao, Yao Jiamin, et al. Feature combination via importance-inhibition analysis [J]. Journal of Southeast University (English Edition), 2013, 29 (1): 22-26. [doi:10.3969/j.issn.1003-7985.2013.01.005]

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 1

Page:

22-26

Research Field:

Computer Science and Engineering

Publishing date:

2013-03-20

Info

Title:

Feature combination via importance-inhibition analysis

Author(s):

Yang Sichun¹;  2;  Gao Chao³;  Yao Jiamin²;  Dai Xinyu¹;  Chen Jiajun¹

¹State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
²School of Computer Science, Anhui University of Technology, Maanshan 243032, China
³School of Computer Science and Information Engineering, Chuzhou University, Chuzhou 239000, China

Keywords:

question answering system;  question classification;  feature combination;  importance-inhibition analysis

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2013.01.005

Abstract:

A new method for combining features via importance-inhibition analysis(IIA)is described to obtain more effective feature combination in learning question classification. Features are combined based on the inhibition among features as well as the importance of individual features. Experimental results on the Chinese questions set show that, the IIA method shows a gradual increase in average and maximum accuracies at all feature combinations, and achieves great improvement over the importance analysis(IA)method on the whole. Moreover, the IIA method achieves the same highest accuracy as the one by the exhaustive method, and further improves the performance of question classification.

References:

[1] Zhang Z C, Zhang Y, Liu T, et al. Advances in open-domain question answering [J]. Acta Electronica Sinica, 2009, 37(5):1058-1069.(in Chinese)
[2] Li X, Roth D. Learning question classifiers[C]//Proc of the 19th International Conference on Computational Linguistics. Taipei, China, 2002: 1-7.
[3] Li X, Roth D. Learning question classifiers: the role of semantic information[J]. Journal of Natural Language Engineering, 2006, 12(3): 229-250.
[4] Zhang D, Lee W. Question classification using support vector machines[C]//Proc of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Toronto, Canada, 2003: 26-32.
[5] Huang Z H, Thint M, Qin Z C. Question classification using head words and their hypernyms[C]//Proc of the 2008 Conference on Empirical Methods in Natural Language Processing. Honolulu, Hawaii, USA, 2008: 927-936.
[6] Huang Z H, Thint M, Celikyilmaz A. Investigation of question classifier in question answering[C]//Proc of the 2009 Conference on Empirical Methods in Natural Language Processing. Singapore, 2009: 543-550.
[7] Li F T, Zhang X, Yuan J H, et al. Classifying what-type questions by head noun tagging[C]//Proc of the 22nd International Conference on Computational Linguistics. Manchester, UK, 2008: 481-488.
[8] Li X, Huang X J, Wu L D. Combined multiple classifiers based on TBL algorithm and their application in question classification [J]. Journal of Computer Research and Development, 2008, 45(3): 535-541.(in Chinese)
[9] Sun J G, Cai D F, Lu D X, et al. HowNet based Chinese question automatic classification [J]. Journal of Chinese Information Processing, 2007, 21(1):90-95.(in Chinese)
[10] Zhang Z C, Zhang Y, Liu T, et al. Chinese question classification based on identification of cue words and extension of training set [J]. Chinese High Technology Letters, 2009, 19(2): 111-118.(in Chinese)

Memo

Memo:

Biographies: Yang Sichun(1970—), male, graduate; Chen Jiajun(corresponding author), male, doctor, professor, chenjj@nju.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61003112, 61170181), the Open Research Fund of State Key Laboratory for Novel Software Technology of China(No.KFKT2010B02), the Key Project of Natural Science Research for Anhui Colleges of China(No.KJ2011A048).
Citation: Yang Sichun, Gao Chao, Yao Jiamin, et al. Feature combination via importance-inhibition analysis[J].Journal of Southeast University(English Edition), 2013, 29(1):22-26.[doi:10.3969/j.issn.1003-7985.2013.01.005]

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