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Journal Article Effects of Answer Weight Boosting in Strategy-driven Question Answering
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Authors
Hyo-Jung Oh, Sung Hyon Myaeng, Myung-Gil Jang
Issue Date
2012-01
Citation
Information Processing & Management, v.48, no.1, pp.83-93
ISSN
0306-4573
Publisher
Elsevier
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1016/j.ipm.2011.01.010
Abstract
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. © 2011 Elsevier Ltd. All rights reserved.
KSP Keywords
Fine grained(FG), Natural Language Processing(NLP), QA system, boosting method, question answering