Improving Question Recommendation by Exploiting Information Need

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1425–1434, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Improving Question Recommendation by Exploiting Information Need Shuguang Li Department of Computer Science University of York, YO10 5DD, UK sgli@cs.york.ac.uk Suresh Manandhar Department of Computer Science University of York, YO10 5DD, UK suresh@cs.york.ac.uk Abstract In this paper we address the problem of ques- tion recommendation from large archives of community question answering data by ex- ploiting the users’ information needs. Our experimental results indicate that questions based on the same or similar information need can provide excellent question recommenda- tion. We show that translation model can be effectively utilized to predict the information need given only the user’s query question. Ex- periments show that the proposed information need prediction approach can improve the per- formance of question recommendation. 1 Introduction There has recently been a rapid growth in the num- ber of community question answering (CQA) ser- vices such as Yahoo! Answers1, Askville2 and WikiAnswer3 where people answer questions post- ed by other users. These CQA services have built up very large archives of questions and their answers. They provide a valuable resource for question an- swering research. Table 1 is an example from Ya- hoo! Answers web site. In the CQA archives, the title part is the user’s query question, and the user’s information need is usually expressed as natural lan- guage statements mixed with questions expressing their interests in the question body part. In order to avoid the lag time involved with wait- ing for a personal response and to enable high quali- 1http://answers.yahoo.com 2http://askville.amazon.com 3http://wiki.answers.com ty answers from the archives to be retrieved, we need to search CQA archives of previous questions that are closely associated with answers. If a question is found to be interesting to the user, then a previ- ous answer can be provided with very little delay. Question search and question recommendation are proposed to facilitate finding highly relevant or po- tentially interesting questions. Given a user’s ques- tion as the query, question search tries to return the most semantically similar questions from the question archives. As the complement of question search, we define question recommendation as rec- ommending questions whose information need is the same or similar to the user’s original question. For example, the question “What aspects of my com- puter do I need to upgrade ...” with the informa- tion need “... making a skate movie, my computer freezes, ...” and the question “What is the most cost effective way to expend memory space ...” with in- formation need “... in need of more space for mu- sic and pictures ...” are both good recommendation questions for the user in Table 1. So the recommend- ed questions are not necessarily identical or similar to the query question. In this paper, we discuss methods for question recommendation based on using the similarity be- tween information need in the archive. We also propose two models to predict the …

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