The week's pick
Reseach Article
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.
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