Question Classification using Naive Bayes Classifier and Creating Missing Classes using Semantic Similarity in Question Answering System
 |
International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2015 by IJETT Journal | ||
| Volume-23 Number-4 |
||
| Year of Publication : 2015 | ||
| Authors : Jeena Mathew, Shine N Das |
||
| DOI : 10.14445/22315381/IJETT-V23P231 |
Citation
Jeena Mathew, Shine N Das"Question Classification using Naive Bayes Classifier and Creating Missing Classes using Semantic Similarity in Question Answering System", International Journal of Engineering Trends and Technology (IJETT), V23(4),155-160 May 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Question Classification is the core component of the Question Answering System. The quality of the question answering system depends on the results of the question classification. Almost all the question classification algorithms are based on the classes defined by Li and Roth [2].In this paper, a question classification algorithm based on Naïve Bayes Classifier and question semantic similarity is proposed. This paper mainly focuses on Numeric and Location type questions. Naive Bayes Classifier is adopted to classify the questions into Numeric and Location classes and semantic similarity is used to classify the questions into their fine-grained classes. According to Li and Roth, the coarse grained class Numeric and Location has fine-grained class Other. In this paper, we also present the method to replace the Other class in Numeric and Location classes by creating new classes and adding the newly created classes in the hierarchy.
References
[1] Z. Wu and M. Palmer, ?Verbs semantics and lexical selection, in Proceedings of the 32nd annual meeting on Association for Computational Linguistics, ser. ACL ‘94. Stroudsburg, PA, USA: Association for Computational Linguistics, 1994, pp. 133–138. [Online]. Available: http://dx.doi.org/10.3115/981732.981751
[2] X. Li and D. Roth, ?Learning question classifiers, in Proceedings of the 19th international conference on Computational linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2002, pp. 1–7.
[3] X. Li and D. Roth, ?Learning question classifiers: the role of semantic information, Natural Language Engineering, vol. 12, no. 03, pp. 229– 249,2006.[Online].Available:http://dx.doi.org/10.1017/S135132490500 3955
[4] L.A.Zadeh, "From search engines to question answering systems—The problems of world knowledge, relevance, deduction and precisiation." Capturing Intelligence 1 (2006): 163-210.
[5] H.Sundblad, "Question Classification in Question Answering Systems." (2007).
[6] M.Bakhtyar and A.Kawtrakul, ?Integrating knowledge resources and shallow language processing for question classification, in Proceedings of the KRAQ11 workshop. Chiang Mai: Asian Federation of Natural Language Processing, November 2011, pp. 22–28. [Online]. Available: http://www.aclweb.org/anthology/W11-3104
[7] M.Bakhtyar et al. "Creating missing classes automatically to improve question classification in question answering systems." Digital Information Management (ICDIM), 2012 Seventh International Conference on. IEEE, 2012.
[8] Xu.Jinzhong, Y.Zhou, and Y.Wang. "A classification of questions using SVM and semantic similarity analysis." Internet Computing for Science and Engineering (ICICSE), 2012 Sixth International Conference on. IEEE, 2012.
Keywords
Naïve Bayes Classifier, Natural Language Processing, Question Answering, Question Class Hierarchy, Question Classification, Semantic Similarity.