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Survey Paper on a Pairwise Learning to Rank Model for Answer Selection in Community Question Answer (CQA) System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 38 |
| Year of Publication: 2018 |
| Authors: Rahul Patil, Sunny Shah, Tejas Bavaskar, Sourabh Ukhale, Akash Kalyankar |
10.5120/ijca2018916803
|
Rahul Patil, Sunny Shah, Tejas Bavaskar, Sourabh Ukhale, Akash Kalyankar . Survey Paper on a Pairwise Learning to Rank Model for Answer Selection in Community Question Answer (CQA) System. International Journal of Computer Applications. 179, 38 ( Apr 2018), 11-14. DOI=10.5120/ijca2018916803
Abstract
To find the similar questions is very difficult in Question Answering (QA) System. Because each question in the returned candidate pool consists of multiple answers, and hence users get trouble to browse a lot before finding the correct one. To overcome this problem, we construct a novel approach a novel Pair wise learning to rank model i.e PLANE which can quantitatively rank answer candidates from the relevant question pool. Specifically, it comprises two components i.e. one offline learning component and one online search component. In the offline learning component, we first consequently set up the positive, neutral, and negative training samples in the forms of preference pairs guided by our data-driven observations. We at that point display a novel model to together consolidate these three sorts of preparing tests and the closed-form solution of this model is determined. In the online search component, we initially gather a pool of answer candidates for the given question by means of discovering its comparable or similar questions. We at that point sort the appropriate answer candidates by utilizing the offline trained model to judge the preference orders. We also design recommendation system, in which best solution is recommended. The system also provides facilities like bookmarking as well as sends best answer on email. Our model is robust as well as achieves better performance than several state-of-the-art answer selection baselines.
References
- W. Wei, Z. Ming, L. Nie, G. Li, J. Li, F. Zhu, T. Shang, and C. Luo, “Exploring heterogeneous features for query-focused summarization of categorized community answers,” Inf. Sci., vol. 330, pp. 403–423, 2016.
- X. Li, Y. Ye, and M. K. Ng, “Multivcrank with applications to image retrieval,” TIP, vol. 25, no. 3, pp. 1396–1409, 2016.
- W. Wei, G. Cong, C. Miao, F. Zhu, and G. Li, “Learning to find topic experts on twitter via different relations,” TKDE, vol. 28, no. 7, pp. 1764–1778, 2016
- W. Wei, B. Gao, T. Liu, T. Wang, G. Li, and H. Li, “A ranking approach on a large-scale graph with multidimensional heterogeneous information,” TOC, vol. 46, no. 4, pp. 930–944, 2016.
- X. Wei, H. Huang, C. Lin, X. Xin, X. Mao, and S. Wang, “Reranking voting-based answers by discarding user behavior biases,” in Proceedings of IJCAI’15, 2015, pp. 2380–2386.
- Q. H. Tran, V. Duc, Tran, T. T. Vu, M. L. Nguyen, and S. B. Pham, “Jaist: Combining multiple features for answer selection in community question answering,” in Proceedings of SemEval’15. ACL, 2015, pp. 215C–219.
- Savenkov, “Ranking answers and web passages for non-factoid question answering: Emory university at TREC liveqa,” in Proceedings of TREC’15, 2015.
- L. Nie, Y. Zhao, X. Wang, J. Shen, and T. Chua, “Learning to recommend descriptive tags for questions in social forums,” TOIS, vol. 32, no. 1, p. 5, 2014.
- Z. Ji and B. Wang, “Learning to rank for question routing in community question answering,” in Proceedings of CIKM’13. ACM, 2013, pp. 2363–2368
- D. H. Dalip, M. A. Gonc¸alves, M. Cristo, and P. Calado, “Exploiting user feedback to learn to rank answers in qaforums: A case study with stack overflow,” in Proceedings of SIGIR’13. ACM, 2013, pp. 543–552.
- T. C. Zhou, M. R. Lyu, and I. King, “A classificationbased approach to question routing in community question answering,” in Proceedings of WWW’12. ACM, 2012, pp. 783–790.
- A. Agarwal, H. Raghavan, K. Subbian, P. Melville, R. D. Lawrence, D. C. Gondek, and J. Fan, “Learning to rank for robust question answering,” in Proceedings of CIKM ’12. ACM, 2012, pp. 833–842.
- Hieber and S. Riezler, “Improved answer ranking in social question-answering portals,” in Proceedings of SMUC’11.ACM, 2011, pp. 19–26.
- B. Li and I. King, “Routing questions to appropriate answerers in community question answering services,” in Proceedings of CIKM’10. ACM, 2010, pp. 1585–1588.
- K. Wang, Z. Ming, and T.-S. Chua, “A syntactic tree matching approach to finding similar questions in community-based qa services,” in Proceedings of SIGIR’09. ACM, 2009, pp. 187–194.
- M. A. M. L. Wei Ding, He Jiang, Modern Advances in Intelligent Systems and Tools, ser. SCI. Springer, 2012, vol. 431.
- E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, “Finding high-quality content in social media,” in Proceedings of WSDM’08. ACM, 2008, pp. 183–194.
- J. Jeon, W. B. Croft, J. H. Lee, and S. Park, “A framework to predict the quality of answers with non-textual features,” in Proceedings of SIGIR’06. ACM, 2006, pp. 228–235.
- Z. Ji and B. Wang, “Learning to rank for question routing in community question answering,” in Proceedings of CIKM’13. ACM, 2013, pp. 2363–2368.
- T. C. Zhou, M. R. Lyu, and I. King, “A classification based approach to question routing in community question answering,” in Proceedings of WWW’12. ACM, 2012, pp. 783– 790.
- L. Yang, M. Qiu, S. Gottipati, F. Zhu, J. Jiang, H. Sun, and Z. Chen, “Cqarank: Jointly model topics and expertise in community question answering,” in Proceedings of CIKM’13. ACM, 2013, pp. 99–108.
- B. Li and I. King, “Routing questions to appropriate answerers in community question answering services,” in Proceedings of CIKM’10. ACM, 2010, pp. 1585–1588.
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