A Comprehensive Retrospection of Literature Reported Works of Community Question Answering Systems
Venkateswara Rao. P1, A.P Siva Kumar2
1Mr. Venkateswara Rao P, Assistant Professor, Department of CSE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.
2Dr. A. P. Sivakumar, Assistant Professor, Department of CSE, JNTU Anantapur, Andhra Pradesh, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1904-1917 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8769019320/2020©BEIESP | DOI: 10.35940/ijitee.C8769.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Community question answering CQA) systems are rapidly gaining attention in the society. Several researchers have actively engaged in improving the theories associated with question answering (QA) systems. This paper reviews the literature reported works on question answering QA systems. In this paper, we discuss on the early contributions on QA systems along with their present and future scope. We have categorized the literature reported works into 20 subgroups according to their significance and relevance. The works in each group will be brought out along with their inter-relevance. Finding the question and answer quality is the prime challenge almost addressed by many researchers. Modeling similar questions, identifying experts in prior and understanding seeker satisfaction also considered as potential challenges. Researchers at the most have done experimentations on popular CQAs like Yahoo! Answers, Wiki Answers, Baidu Knows, Brianly, Quora, Pubmed and Stack Overflow respectively. Machine learning, probabilistic modeling, deep learning and hybrid approach of solving show profound significance in addressing various challenges encounter with QA systems. Today the paradigm of CQA systems took the shift by serving as Open Educational Resources to learning community.
Keywords: Community Question Answering Systems (CQAs), Question Answer Retrieval, Question Analysis.
Scope of the Article: Community Information Systems