Visible to the public A Practical Approach to Answer Extraction for Constructing QA Solution

TitleA Practical Approach to Answer Extraction for Constructing QA Solution
Publication TypeConference Paper
Year of Publication2018
AuthorsXiong, M., Li, A., Xie, Z., Jia, Y.
Conference Name2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)
Date Publishedjun
Keywordsagricultural expert, Agriculture, answer extraction, answer quality assessment, candidate answers, composability, high-quality answers, information extraction, Internet, Internet users, Knowledge discovery, metadata, Metadata Discovery Problem, modern agricultural field, Portals, pubcrawl, QA solution construction, Query Extension, question answering (information retrieval), question answering system, question-answer pairs, RCAS, Resiliency, Scalability, search engines, semantic gaps, Semantics, straight lexical gaps, support sets, Yahoo
AbstractQuestion Answering system(QA) plays an increasingly important role in the Internet age. The proportion of using the QA is getting higher and higher for the Internet users to obtain knowledge and solve problems, especially in the modern agricultural filed. However, the answer quality in QA varies widely due to the agricultural expert's level. Answer quality assessment is important. Due to the lexical gap between questions and answers, the existing approaches are not quite satisfactory. A practical approach RCAS is proposed to rank the candidate answers, which utilizes the support sets to reduce the impact of lexical gap between questions and answers. Firstly, Similar questions are retrieved and support sets are produced with their high-quality answers. Based on the assumption that high quality answers would also have intrinsic similarity, the quality of candidate answers are then evaluated through their distance from the support sets. Secondly, Different from the existing approaches, previous knowledge from similar question-answer pairs are used to bridge the straight lexical and semantic gaps between questions and answers. Experiments are implemented on approximately 0.15 million question-answer pairs about agriculture, dietetics and food from Yahoo! Answers. The results show that our approach can rank the candidate answers more precisely.
Citation Keyxiong_practical_2018