Volume 13, Issue 3 (12-2016)                   JSDP 2016, 13(3): 99-112 | Back to browse issues page

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Ferdowsi University of Mashhad (FUM)
Abstract:   (3964 Views)

Question answering systems are produced and developed to provide exact answers to the question posted in natural language. One of the most important parts of question answering systems is question classification. The purpose of question classification is predicting the kind of answer needed for the question in natural language. The  literature works can be categorized as rule-based and learning-based methods. This paper proposes a novel architecture for hybrid classification of questions. The results of the classifiers were combined by five methods of Weighted Voting, Behavior Knowledge space, Naive Bayes, Decision Template and Dempster-Shafer. The method uses a combination of two classifiers based on machine learning (Support Vector Machine and Sparse Representation) and one rule-based classifier. The learning-based classification uses the set of features extracted from the questions. The features are extracted on the basis of the lexical and syntactic structure of the questions. The results from the classifiers were combined by the methods that are common in the combination of one-class classifiers and the Obtained results indicate the improvement of the classification operations in comparison with the present methods. 

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Type of Study: Research | Subject: Paper
Received: 2014/09/15 | Accepted: 2016/09/7 | Published: 2017/04/23 | ePublished: 2017/04/23

1. [1] S.H. Nabavi karizi, & E. Kabir, "Combining classifiers: Diversifying and rules of composition." CSI Journal on Computer Science and Engineering, vol. 3, pp. 95. Autumn 1384
2. [2] M. Collins, "Head-driven statistical models for natural language parsing." Computational linguistics, vol. 29, pp. 589-637, Dec 2003. [DOI:10.1162/089120103322753356]
3. [3] J.R. Finkel, T. Grenager, and C. Manning, "Incorporating non-local information into information extraction systems by gibbs sampling," In Association for Computational Linguistics: Proceedings of the 43rd annual meeting on association for computational linguistics, Michigan: ACL, 2005. pp. 363-370. [DOI:10.3115/1219840.1219885]
4. [4] Ulf. Hermjakob, "Parsing and question classification for question answering," In Association for Computational Linguistics: Proceedings of the workshop on Open-domain question answering, Vol. 12, France: ACL, 2001, pp. 1-6.
5. [5] T.K. Ho, J.J. Hull, and S.N. Srihari, "Combination of decisions by multiple classifiers." In Structured document image analysis, Springer Berlin Heidelberg 1992, pp. 188-202. [DOI:10.1007/978-3-642-77281-8_8]
6. [6] Hoque, M. Moinul, T. Goncalves, and P. Quaresma, "Classifying Questions in Question Answering System Using Finite State Machines with a Simple Learning Approach." In PACLIC Vol. 27, Taiwan 2013 pp. 409-414.
7. [7] Z. Huang, M. Thint, and Z. Qin, "Question classification using head words and their hypernyms." In Association for Computational Linguistics: Proceedings Conference on Empirical Methods in Natural Language Processing, Hawaii USA: ACL 2008, pp. 927-936. [DOI:10.3115/1613715.1613835]
8. [8] V. Krishnan, Das. Sujatha, and S. Chakrabarti, "Enhanced answer type inference from questions using sequential models." In Association for Computational Linguistics: Proceedings Conference on Empirical Methods in Natural Language Processing, Vancouver Canada: ACL 2005, pp. 315-322.
9. [9] Kuncheva, I. Ludmila, Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2004. [DOI:10.1002/0471660264]
10. [10] Li, Xin, and D. Roth, "Learning question classifiers." In Association for Computational Linguistics: COLING '02 Proceedings of the 19th international conference on Computational linguistics, Taipei, Taiwan: COLING 2002, pp. 1-7. [DOI:10.3115/1072228.1072378]
11. [11] B. Loni, S. H. Khoshnevis, and P. Wiggers, "Latent semantic analysis for question classification with neural networks." In Automatic Speech Recognition and Understanding, Hilton Waikoloa: ASRU, 2011, pp. 437-447.
12. [12] M. C. Marneffe, B. MacCartney, and D. Christopher, "Generating typed dependency parses from phrase structure parses." In Conferences bring together a large number of people working and interested in HLT Proceedings: LREC, Genoa, Italy: May 2006, pp. 449-454.
13. [13] A. Merkel, and D. Klakow, "Improved methods for language model based question classification." In INTERSPEECH, Antwerp, Belgium :August 2007, pp. 322-325.
14. [14] D. Moldovan, M. Paşca, S. Harabagiu, & M. Surdeanu, "Performance issues and error analysis in an open-domain question answering system." ACM Transactions on Information Systems, vol. 21, pp. 133-154, October 2003. [DOI:10.1145/763693.763694]
15. [15] Y. Pan, Y. Tang, L. Lin, & Y. Luo, "Question classification with semantic tree kernel." In ACM SIGIR conference on Research and development in information retrieval: Proceedings of the 31st annual international, Singapore, Singapore, July 2008, pp. 837-838. [DOI:10.1145/1390334.1390530]
16. [16] D. Radev, W. Fan, H. Qi, H. Wu, & A.Grewal, "Probabilistic question answering on the web," Journal of the American Society for Information Science and Technology, vol. 56, pp. 571-583, DEC 2005. [DOI:10.1002/asi.20146]
17. [17] S. K. Ray, S. Singh, & B. P. Joshi, "A semantic approach for question classification using WordNet and Wikipedia." Pattern Recognition Letters, vol. 31, pp. 1935-1943, October 2010. [DOI:10.1016/j.patrec.2010.06.012]
18. [18] J. Silva, L.Coheur, A. C. Mendes, & A. Wichert, "From symbolic to sub-symbolic information in question classification." Artificial Intelligence Review, vol. 35, pp. 137-154, February 2011. [DOI:10.1007/s10462-010-9188-4]
19. [19] H. Sundblad, "Question classification in question answering systems". Phd dissertation,. Institutionen för datavetenskap, 2007.
20. [20] K. Toutanova, D. Klein, C.D. Manning, & Y. Singer, "Feature-rich part-of-speech tagging with a cyclic dependency network". In Association for Computational Linguistics on Human Language Technology: Proceedings of the Conference of the North American Chapter, Washington, DC, USA, October 2003, pp. 173-180. [DOI:10.3115/1073445.1073478]
21. [21] J. Wright, et al. "Robust face recognition via sparse representation." IEEE transactions on pattern analysis and machine intelligence, vol 31, pp. 210-227, Feb 2009. [DOI:10.1109/TPAMI.2008.79] [PMID]

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