Answering Opinion Questions with Random Walks on Graphs

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 737–745, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Answering Opinion Questions with Random Walks on Graphs Fangtao Li, Yang Tang, Minlie Huang, and Xiaoyan Zhu State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Sci. and Tech., Tsinghua University, Beijing 100084, China {fangtao06,tangyang9}@gmail.com,{aihuang,zxy-dcs}@tsinghua.edu.cn Abstract Opinion Question Answering (Opinion QA), which aims to find the authors’ sen- timental opinions on a specific target, is more challenging than traditional fact- based question answering problems. To extract the opinion oriented answers, we need to consider both topic relevance and opinion sentiment issues. Current solu- tions to this problem are mostly ad-hoc combinations of question topic informa- tion and opinion information. In this pa- per, we propose an Opinion PageRank model and an Opinion HITS model to fully explore the information from different re- lations among questions and answers, an- swers and answers, and topics and opin- ions. By fully exploiting these relations, the experiment results show that our pro- posed algorithms outperform several state of the art baselines on benchmark data set. A gain of over 10% in F scores is achieved as compared to many other systems. 1 Introduction Question Answering (QA), which aims to pro- vide answers to human-generated questions auto- matically, is an important research area in natu- ral language processing (NLP) and much progress has been made on this topic in previous years. However, the objective of most state-of-the-art QA systems is to find answers to factual questions, such as “What is the longest river in the United States?” and “Who is Andrew Carnegie?” In fact, rather than factual information, people would also like to know about others’ opinions, thoughts and feelings toward some specific objects, people and events. Some examples of these questions are: “How is Bush’s decision not to ratify the Kyoto Protocol looked upon by Japan and other US al- lies?”(Stoyanov et al., 2005) and “Why do peo- ple like Subway Sandwiches?” from TAC 2008 (Dang, 2008). Systems designed to deal with such questions are called opinion QA systems. Re- searchers (Stoyanov et al., 2005) have found that opinion questions have very different character- istics when compared with fact-based questions: opinion questions are often much longer, more likely to represent partial answers rather than com- plete answers and vary much more widely. These features make opinion QA a harder problem to tackle than fact-based QA. Also as shown in (Stoy- anov et al., 2005), directly applying previous sys- tems designed for fact-based QA onto opinion QA tasks would not achieve good performances. Similar to other complex QA tasks (Chen et al., 2006; Cui et al., 2007), the problem of opinion QA can be viewed as a sentence ranking problem. The Opinion QA task needs to consider not only the topic relevance of a sentence (to identify whether this sentence matches the topic of the question) but also …

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