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

doi:10.1587/transinf.E95.D.2009

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

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E95-D No.7 pp.2009-2012

Publication Date: 2012/07/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E95.D.2009

Type of Manuscript: LETTER

Category: Data Engineering, Web Information Systems

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Yeo-Chan YOON, Chang-Ki LEE, Hyun-Ki KIM, Myung-Gil JANG, Pum Mo RYU, So-Young PARK, "Descriptive Question Answering with Answer Type Independent Features" in IEICE TRANSACTIONS on Information, vol. E95-D, no. 7, pp. 2009-2012, July 2012, doi: 10.1587/transinf.E95.D.2009.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2009/_p

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@ARTICLE{e95-d_7_2009,
author={Yeo-Chan YOON, Chang-Ki LEE, Hyun-Ki KIM, Myung-Gil JANG, Pum Mo RYU, So-Young PARK, },
journal={IEICE TRANSACTIONS on Information},
title={Descriptive Question Answering with Answer Type Independent Features},
year={2012},
volume={E95-D},
number={7},
pages={2009-2012},
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.},
keywords={},
doi={10.1587/transinf.E95.D.2009},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - Descriptive Question Answering with Answer Type Independent Features
T2 - IEICE TRANSACTIONS on Information
SP - 2009
EP - 2012
AU - Yeo-Chan YOON
AU - Chang-Ki LEE
AU - Hyun-Ki KIM
AU - Myung-Gil JANG
AU - Pum Mo RYU
AU - So-Young PARK
PY - 2012
DO - 10.1587/transinf.E95.D.2009
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2012
AB - 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.
ER -