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10.5120/ijca2016912568 |
Divya Panicker and Archana Chaugule. Impact of Question Classification on Accuracy of Question Answering System. International Journal of Computer Applications 156(11):31-34, December 2016. BibTeX
@article{10.5120/ijca2016912568, author = {Divya Panicker and Archana Chaugule}, title = {Impact of Question Classification on Accuracy of Question Answering System}, journal = {International Journal of Computer Applications}, issue_date = {December 2016}, volume = {156}, number = {11}, month = {Dec}, year = {2016}, issn = {0975-8887}, pages = {31-34}, numpages = {4}, url = {http://www.ijcaonline.org/archives/volume156/number11/26755-2016912568}, doi = {10.5120/ijca2016912568}, publisher = {Foundation of Computer Science (FCS), NY, USA}, address = {New York, USA} }
Abstract
Question answering system provides the user with functionality to get precise answer of the question articulated in their natural language. Question classification is a vital part of any question answering system. The accuracy of answer provide by question answering system heavily depends on the way question is classified. Accurate question classification leads to retrieval of exact answer in question answering system. Extracting whether a question is subjective or objective helps in analyzing the actual structure of answer expected by the users.
References
- M. R. Morris, J. Teevan, and K. Panovich, “What do people ask their social networks, and why?: A survey study of status message q&a behavior,” in Proc. SIGCHI Conf. Human Factors Comput. Syst., 2010, pp. 1739–1748.
- S. A. Paul, L. Hong, and E. H. Chi, “What is a question? Crowdsourcing tweet categorization,” in Proc. SIGCHI Conf. Human Factors Comput. Syst., 2011, pp. 578–581.
- F. M. Harper, D. Moy, and J. A. Konstan, “Facts or friends?: Distinguishing informational and conversational questions in social Q&A sites,” in Proc. SIGCHI Conf. Human Factors Comput. Syst., 2009, pp. 759–768.
- A. Pal, J. Margatan, and J. Konstan, “Question temporality: Identification and uses,” in Proc. ACM Conf. Comput. Supported Cooperat. Work,2012, pp. 257–260.
- M. Jiang and S. Argamon, “Exploiting subjectivity analysis in blogs to improve political leaning categorization,” in Proc. 31st Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., 2008, pp. 725–726.
- P. Biyani, C. Caragea, and P. Mitra, “Predicting subjectivity orientation of online forum threads,” in Proc. Comput. Linguistics Intell. Text Process., 2013, pp. 109–120.
- B. Li, Y. Liu, A. Ram, E. V. Garcia, and E. Agichtein, “Exploring question subjectivity prediction in community QA,” in Proc. 31st Annu.Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., 2008, pp. 735–736.
- L. Chen, D. Zhang, and L. Mark, “Understanding user intent in community question answering,” in Proc. 21st Int. Conf. Companion World Wide Web, 2012, pp. 823–828.
- J. Wiebe and E. Riloff, “Creating subjective and objective sentence classifiers from unannotated texts,” in Proc. 6th Int. Conf. Comput.Linguistics Intell. Text Process., 2005, pp. 486–497.
- C. Lin, Y. He, and R. Everson, “Sentence subjectivity detection with weakly-supervised learning,” in Proc. 5th Int. Joint Conf. Natural Lang.Process., Nov. 2011, pp. 1153–1161.
- Y. Liu, J. Bian, and E. Agichtein, “Predicting information seeker satisfaction in community question answering,” in Proc. 31st Annu. Int.ACM SIGIR Conf. Res. Develop. Inf. Retr., 2008, pp. 483–
- Z. Liu and B. J. Jansen, “Factors influencing the response rate in social question and answering behavior,” in Proc. Conf. Comput. Supported Cooperat. Work, 2013, pp. 1263–1274.
- Anderson, Ashton, et al. "Discovering value from community activity on focused CQA sites: a case study of stack overflow."Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining.ACM, 2012
Keywords
Question answering, Question classification, Information retrieval, Natural Language Processing, neural networks.