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Descriptive Question Answering with Answer Type Independent Features
Yeo-Chan YOON Chang-Ki LEE Hyun-Ki KIM Myung-Gil JANG Pum Mo RYU So-Young PARK
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2012/07/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Data Engineering, Web Information Systems
descriptive question answering,
Full Text: PDF(575.8KB)>>
In this paper, we present a supervised learning method to seek out answers to the most frequently asked descriptive questions: reason, method, and definition questions. Most of the previous systems for question answering focus on factoids, lists or definitional questions. However, descriptive questions such as reason questions and method questions are also frequently asked by users. We propose a system for these types of questions. The system conducts an answer search as follows. First, we analyze the user's question and extract search keywords and the expected answer type. Second, information retrieval results are obtained from an existing search engine such as Yahoo or Google . Finally, we rank the results to find snippets containing answers to the questions based on a ranking SVM algorithm. We also propose features to identify snippets containing answers for descriptive questions. The features are adaptable and thus are not dependent on answer type. Experimental results show that the proposed method and features are clearly effective for the task.