상세정보
학술지
Effects of Answer Weight Boosting in Strategy-driven Question Answering
- 발행일
- 201201
- 출처
- Information Processing & Management, v.48 no.1, pp.83-93
- ISSN
- 0306-4573
- 출판사
- Elsevier
- 초록
- 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 제안 키워드
- Natural Language Processing, QA system, boosting method, fine-grained, question answering