Effects of answer weight boosting in strategy-driven question answering

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With the advances in natural language processing (NLP) techniques and the need to deliver more fine-grained information or answers than a set of documents, various QA techniques have been developed corresponding to different question and answer types. A comprehensive QA system must be able to incorporate individual QA techniques as they are developed and integrate their functionality to maximize the system's overall capability in handling increasingly diverse types of questions. To this end, a new QA method was developed to learn strategies for determining module invocation sequences and boosting answer weights for different types of questions. In this article, we examine the roles and effects of the answer verification and weight boosting method, which is the main core of the automatically generated strategy-driven QA framework, in comparison with a strategy-less, straightforward answer-merging approach and a strategy-driven but with manually constructed strategies. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2012-01
Language
English
Article Type
Article
Citation

INFORMATION PROCESSING & MANAGEMENT, v.48, no.1, pp.83 - 93

ISSN
0306-4573
DOI
10.1016/j.ipm.2011.01.010
URI
http://hdl.handle.net/10203/97127
Appears in Collection
CS-Journal Papers(저널논문)
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