상세정보
학술지
Semantic Passage Segmentation based on Sentence Topics for Question Answering
- 발행일
- 200709
- 출처
- Information Sciences, v.177 no.18, pp.3696-3717
- ISSN
- 0020-0255
- 출판사
- Elsevier
- 협약과제
- 07MW1700, 신성장동력산업용 대용량 대화형 분산 처리 음성인터페이스 기술개발, 이윤근
- 초록
- We propose a semantic passage segmentation method for a Question Answering (QA) system. We define a semantic passage as sentences grouped by semantic coherence, determined by the topic assigned to individual sentences. Topic assignments are done by a sentence classifier based on a statistical classification technique, Maximum Entropy (ME), combined with multiple linguistic features. We ran experiments to evaluate the proposed method and its impact on application tasks, passage retrieval and template-filling for question answering. The experimental result shows that our semantic passage retrieval method using topic matching is more useful than fixed length passage retrieval. With the template-filling task used for information extraction in the QA system, the value of the sentence topic assignment method was reinforced. © 2007 Elsevier Inc. All rights reserved.
- KSP 제안 키워드
- Classification techniques, Experimental Result, Passage retrieval, Passage segmentation, QA system, Semantic coherence, Statistical classification, Topic assignment, information extraction, linguistic features, maximum entropy(ME)