A community question-answering refinement system

Community Question Answering (CQA) websites, which archive millions of questions and answers created by CQA users to provide a rich resource of information that is missing at web search engines and QA websites, have become increasingly popular. Web users who search for answers to their questions at CQA websites, however, are often required to either (i) wait for days until other CQA users post answers to their questions which might even be incorrect, offensive, or spam, or (ii) deal with restricted answer sets created by CQA websites due to the exact-match constraint that is employed and imposed between archived questions and user-formulated questions. To automate and enhance the process of locating high-quality answers to a user's question Q at a CQA website, we introduce a CQA refinement system, called QAR. Given Q, QAR first retrieves a set of CQA questions QS that are the same as, or similar to, Q in terms of its specified information need. Thereafter, QAR selects as answers to Q the top-ranked answers (among the ones to the questions in QS) based on various similarity scores and the length of the answers. Empirical studies, which were conducted using questions provided by the Text Retrieval Conference (TREC) and Text Analysis Conference (TAC), in addition to more than four millions questions (and their corresponding answers) extracted from Yahoo! Answers, show that QAR is effective in locating archived answers, if they exist, that satisfy the information need specified in Q. We have further assessed the performance of QAR by comparing its question-matching and answer-ranking strategies with their Yahoo! Answers' counterparts and verified that QAR outperforms Yahoo! Answers in (i) locating the set of questions QS that have the highest degrees of similarity with Q and (ii) ranking archived answers to QS as answers to Q.