Let Other Users Help You Find Answers: A Collaborative Question-Answering Method with Continuous Markov Chain Model

摘要

The answering communities, such as Yahoo! Answers, offer great intelligence to help people solve questions. Participants can express their judgements towards answers and the system also keeps a record for every user. Retrieving Question-Answer pairs (QA pairs) extracted from these forums can improve the quality of Question-Answering (QA) systems. In this paper, we propose a Collaborative Ranking (ColRank) algorithm employing the Continuous Markov Chain Model (CMCM) to combine the quality of QA pairs and relationships among them. Empirical results show that the innovative algorithm is effective and outperform the state of art Question-Answering baselines.