Let Other Users Help You Find Answers: A Collaborative Question-Answering Method With Continuous Markov Chain Model
APWeb'11: Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications(2011)
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 (Col Rank) 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.
更多查看译文
关键词
Question Answering,Collaborative Ranking,Continuous Markov Model
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络