Searching Questions by Identifying Question Topic and Question Focus

Proceedings of ACL-08: HLT, pages 156–164, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Searching Questions by Identifying Question Topic and Question Focus Huizhong Duan1, Yunbo Cao1,2, Chin-Yew Lin2 and Yong Yu1 1Shanghai Jiao Tong University, Shanghai, China, 200240 {summer, yyu}@apex.sjtu.edu.cn 2Microsoft Research Asia, Beijing, China, 100080 {yunbo.cao, cyl}@microsoft.com Abstract This paper is concerned with the problem of question search. In question search, given a question as query, we are to return questions semantically equivalent or close to the queried question. In this paper, we propose to conduct question search by identifying question topic and question focus. More specifically, we first summarize questions in a data structure con- sisting of question topic and question focus. Then we model question topic and question focus in a language modeling framework for search. We also propose to use the MDL- based tree cut model for identifying question topic and question focus automatically. Expe- rimental results indicate that our approach of identifying question topic and question focus for search significantly outperforms the base- line methods such as Vector Space Model (VSM) and Language Model for Information Retrieval (LMIR). 1 Introduction Over the past few years, online services have been building up very large archives of questions and their answers, for example, traditional FAQ servic- es and emerging community-based Q&A services (e.g., Yahoo! Answers1, Live QnA2, and Baidu Zhidao3). To make use of the large archives of questions and their answers, it is critical to have functionality facilitating users to search previous answers. Typi- cally, such functionality is achieved by first re- trieving questions expected to have the same answers as a queried question and then returning the related answers to users. For example, given question Q1 in Table 1, question Q2 can be re- 1 http://answers.yahoo.com 2 http://qna.live.com 3 http://zhidao.baidu.com turned and its answer will then be used to answer Q1 because the answer of Q2 is expected to par- tially satisfy the queried question Q1. This is what we called question search. In question search, re- turned questions are semantically equivalent or close to the queried question. Query: Q1: Any cool clubs in Berlin or Hamburg? Expected: Q2: What are the best/most fun clubs in Berlin? Not Expected: Q3: Any nice hotels in Berlin or Hamburg? Q4: How long does it take to Hamburg from Berlin? Q5: Cheap hotels in Berlin? Table 1. An Example on Question Search Many methods have been investigated for tack- ling the problem of question search. For example, Jeon et al. have compared the uses of four different retrieval methods, i.e. vector space model, Okapi, language model, and translation-based model, within the setting of question search (Jeon et al., 2005b). However, all the existing methods treat questions just as plain texts (without considering question structure). For example, obviously, Q2 can be considered semantically closer to Q1 than Q3-Q5 although all questions (Q2-Q5) are related to Q1. The existing methods are not able to tell the difference between question Q2 and questions Q3, Q4, and Q5 in …

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