BROWSE
Detail
Journal Article
Descriptive Question Answering with Answer Type Independent Features
Cited 0 time in 
Share 
Share
- Authors
- Yeo-Chan YOON, Chang-Ki LEE, Hyun-Ki KIM, Myung-Gil JANG, Pum Mo RYU, So-Young PARK
- Issue Date
- 2012-07
- Citation
- IEICE Transactions on Information and Systems, v.E95.D, no.7, pp.2009-2012
- ISSN
- 0916-8532
- Publisher
- 일본, 전자정보통신학회 (IEICE)
- Language
- English
- Type
- Journal Article
- Abstract
- 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. © 2012 The Institute of Electronics, Information and Communication Engineers.
- KSP Keywords
- Expected Answer Type, Information and communication, SVM algorithm, Search Engine, Supervised learning method, information retrieval, question answering, ranking SVM