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Retrieving Relevant Answer from Large Questions- Answer Dataset by Using Semantic Analysis and Natural Language Processing
| Content Provider | Semantic Scholar |
|---|---|
| Author | Jerlin, Hilda Janarthanan, R. |
| Copyright Year | 2018 |
| Abstract | This paper focuses on addressing the lexical gap problem in question retrieval. Question retrieval in Community question answering(cQA) archives aims to find the existing questions that are semantically equivalent or relevant to the queried questions. However, the lexical gap problem brings a new challenge for question retrieval in cQA. In this paper, we propose to model and learn distributed word representations with metadata of category information within cQA pages for question retrieval using two novel category powered models. One is a basic category powered model called MB-NET and the other one is an enhanced category powered model called ME-NET which can better learn the distributed word representations and alleviate the lexical gap problem. To deal with the variable size of word representation vectors, we employ the framework of fisher kernel to transform them into the fixed-length vectors. Experimental results on large-scale English and Chinese cQA data sets show that our proposed approaches can significantly outperform state-of-the-art retrieval models for question retrieval in cQA. The evaluation results show that promising and significant performance improvements can be achieved. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijirset.com/upload/2018/n3cit/18_anusha%20a%20sec.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
