Abstract
Question Answering Systems (QASs) form an exciting research area as it helps provide the user with the most accurate answer to the input question. For the accuracy of QAS, interpreting the information need of the user is quite pivotal. Thus, question classification forms an imperative module in question answering systems, which will help determine the type of question and its corresponding type of answer. The paper delivers a distinct classification of why-type questions and their corresponding answer types to yield a robust QAS.
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References
Yahoo! Answers. https://in.answers.yahoo.com/, Quora. https://www.quora.com/, Twitter. https://twitter.com/search
Suzan webpage. http://liacs.leidenuniv.nl/~verbernes/
Chen, L., Zhang, D. Mark, L.: Understanding user intent in community question answering. In: Proceedings of the 21st International Conference on World Wide Web. ACM (2012)
Ferret, O., et al.: Finding an answer based on the recognition of the question focus. In: TREC (2001)
Harper, F.M., Moy, D., Konstan, J.A.: Facts or friends?: distinguishing informational and conversational questions in social Q and A sites. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2009)
Moldovan, D., et al.: The structure and performance of an open-domain question answering system. In: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (2000)
Li, F., et al.: Classifying what-type questions by head noun tagging. In: Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1. Association for Computational Linguistics (2008)
Verberne, S.: Developing an approach for why-question answering. In:: Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop. Association for Computational Linguistics (2006)
Jurafsky, D.. Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Prentice-Hall, Englewood Cliffs (2015)
Kim, S., Oh, J.S., Oh, S.: Best-answer selection criteria in a social Q and A site from the user-oriented relevance perspective. In: Proceedings of the Association for Information Science and Technology (2007)
Leech, G., Randolph, Q., Greenbaum, S., Svartvik, J.: A Comprehensive Grammar of the English Language. Longman, London and New York (1985)
Li, B., et al.: Exploring question subjectivity prediction in community QA. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2008)
Verberne, S.: In Search of the Why: Developing a System for Answering Why-Questions. [Sl: sn] (2010)
Mizuno, J., et al.: Non-factoid question answering experiments at NTCIR-6: towards answer type detection for realworld questions. In: NTCIR (2007)
https://research-methodology.net/research-methodology/research-design/exploratory-research/
Verberne, S, et al.: Discourse-based answering of why-questions (2007)
Oostdijk, N.: Using the TOSCA analysis system to analyse a software manual corpus. Ind. Parsing Software Manuals 17, 179 (1996)
http://nursing.utah.edu/research/qualitative-research/what-is-qualitative-research.php
Verberne, S., et al.: Using syntactic information for improving why-question answering. In: Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1. Association for Computational Linguistics (2008)
Liu, Z., Jansen, B.J.: Identifying and predicting the desire to help in social question and answering. Inf. Process. Manag. 53, 490–504 (2017)
Brill, E.: Discovering the lexical features of a language. In: Proceedings of the 29th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (1991)
Oh, J.-H., et al.: Multi-column convolutional neural networks with causality-attention for why-question answering. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM (2017)
Verberne, S., Boves, L.W.J., Oostdijk, N.H.J., Coppen, P.A.J.M.: Discourse-based answering of why-questions (2007)
Xiang, Y., et al.: Answer selection in community question answering via attentive neural networks. IEEE Sig. Process. Lett. 24(4), 505–509 (2017)
Breja, M., Jain, S.K.: Why-type question classification in question answering system. Forum for Information Retrieval Evaluation (2017)
Mann, W.C., Thompson, S.A.: Rhetorical structure theory: description and construction of text structures. In: Kempen, G. (ed.) Natural Language Generation. NATO ASI Series (Series E: Applied Sciences), vol. 135, pp. 85–95. Springer, Dordrecht (1987). https://doi.org/10.1007/978-94-009-3645-4_7
Guy, I., et al.: Identifying informational vs. conversational questions on community question answering archives (2018)
Pearl, J., Mackenzie, D.: The Book of Why: The New Science of Cause and Effect. Penguin, UK (2018)
Kruengkrai, C., et al.: Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks. In: AAAI (2017)
Mohasseb, A., Bader-El-Den, M., Cocea, M.: Question categorization and classification using grammar based approach. Inf. Process. Manag. (2018)
Oh, S.: Social Q&A. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 75–107. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-90092-6_3
Zhou, X., et al.: Recurrent convolutional neural network for answer selection in community question answering. Neurocomputing 274, 8–18 (2018)
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Breja, M., Jain, S.K. (2018). Analysis of Why-Type Questions for the Question Answering System. In: Benczúr, A., et al. New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-00063-9_25
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