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Journal Article Semantic Passage Segmentation based on Sentence Topics for Question Answering
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Authors
Hyo-Jung Oh, Sung Hyon Myaeng, Myung-Gil Jang
Issue Date
2007-09
Citation
Information Sciences, v.177, no.18, pp.3696-3717
ISSN
0020-0255
Publisher
Elsevier
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1016/j.ins.2007.02.038
Project Code
07MW1700, Development of large vocabulary/interactive distributed/embedded VUI for new growth engine industries, Lee Yunkeun
Abstract
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 Keywords
Classification techniques, Experimental Result, Passage retrieval, Passage segmentation, QA system, Semantic coherence, Statistical classification, Topic assignment, information extraction, linguistic features, maximum entropy(ME)