A Structural Support Vector Method for Extracting Contexts and Answers of Questions from Online Forums

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 514–523, Singapore, 6-7 August 2009. c⃝2009 ACL and AFNLP A Structural Support Vector Method for Extracting Contexts and Answers of Questions from Online Forums Wen-Yun Yang †∗ Yunbo Cao †‡ Chin-Yew Lin ‡ † Department of Computer Science and Engineering Shanghai Jiao Tong University, Shanghai, China ‡ Microsoft Research Asia, Beijing, China wenyun.yang@gmail.com {yunbo.cao; cyl}@microsoft.com Abstract This paper addresses the issue of extract- ing contexts and answers of questions from post discussion of online forums. We propose a novel and unified model by customizing the structural Support Vector Machine method. Our customization has several attractive properties: (1) it gives a comprehensive graphical representation of thread discussion. (2) It designs special inference algorithms instead of general- purpose ones. (3) It can be readily ex- tended to different task preferences by varying loss functions. Experimental re- sults on a real data set show that our meth- ods are both promising and flexible. 1 Introduction Recently, extracting questions, contexts and an- swers from post discussions of online forums in- curs increasing academic attention (Cong et al., 2008; Ding et al., 2008). The extracted knowl- edge can be used either to enrich the knowledge base of community question answering (QA) ser- vices such as Yahoo! Answers or to augment the knowledge base of chatbot (Huang et al., 2007). Figure 1 gives an example of a forum thread with questions, contexts and answers annotated. This thread contains three posts and ten sentences, among which three questions are discussed. The three questions are proposed in three sentences, S3, S5 and S6. The context sentences S1 and S2 provide contextual information for question sentence S3. Similarly, the context sentence S4 provides contextual information for question sen- tence S5 and S6. There are three question-context- answer triples in this example, (S3) − (S1, S2) − (S8, S9), (S5) − (S4) − (S10) and (S6) − (S4) − ∗ This work was done while the first author visited Mi- crosoft Research Asia. Post1: <context id=1> S1: Hi I am looking for a pet friendly hotel in Hong Kong because all of my family is going there for vacation. S2: my fam- ily has 2 sons and a dog. </context> <question id=1> S3: Is there any recommended hotel near Sheung Wan or Tsing Sha Tsui? </question> <context id=2, 3> S4: We also plan to go shopping in Causeway Bay. </context> <question id=2> S5: What’s the traffic situation around those com- mercial areas? </question> <question id=3> S6: Is it necessary to take a taxi? </question> S7: Any information would be appreciated. Post2: <answer id=1> S8: The Comfort Lodge near Kowloon Park allows pet as I know, and usu- ally fits well within normal budgets. S9: It is also conveniently located, nearby the Kowloon railway station and subway. </answer> Post3: <answer id=2, 3> S10: It’s very crowd in those areas, so I recommend MTR in Causeway Bay because it is cheap to take you around. </answer> Figure 1: An …

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