1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Probabilistic Model for Definitional Question Answering Graduate : Chen, Shao-Pei Authors : Kyoung-Soo Han, Young-In Song, and Hae-Chang Rim SIGIR

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Methodology Experimental Results Conclusion

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation It is difficult to find which information is useful for the answer to a definitional question. A definitional question such as “What is NASA?”. A short passage cannot answer the definitional questions because a definition needs several essential information about the target.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective  We propose a formal model for definitional QA, considering the characteristics of the definitional questions.  We model the definitional QA from the two points of view, topic and definition. What is NASA? S1: NASA is the agency responsible for the public space program of the USA. S2: NASA was established in 1958. S3: The headquarters of NASA is located in Washington, D.C. S4: NASA announced the new annual budget. S5: John who works for NASA gave a housewarming party yesterday. S6: Ji-Sung Park is a famous football player from South Korea. {S1,S2,S3,S4} are the topic sentences {S1,S2,S3,S6} are the definitional sentences {S1,S2,S3} is the answer to the question.

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Definitional question answering system based on the probabilistic model What is NASA? NASA S1: NASA is the agency responsible for the public space program of the USA. …. Relevant documents to the question target are retrieved, and answer candidates are extracted from the retrieved documents.

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Methodology  General Language Model  Topic Language Model  Definition Language Model Dirichlet smoothing

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Experimental Results The external definitions have almost no noise and the news articles are generally less noisy than web pages. The large difference in the term distribution explains the reason why the system heavily considering the definition type performs so well. The result is slightly underestimated for TREC 2004 questions because ours do not consider other types of questions.

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion  The proposed model can be easily extended to other descriptive QA  For the future work, we will estimate the probabilities of the language models using more contexts. 8