The Use of Dynamic Segment Scoring for Language-Independent Question Answering
MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB
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This paper presents a novel language-independent questionanswering QA system based on natural language processing techniques, shallow query understanding, dynamic sliding window techniques, and statistical proximity distribution matching techniques. The performance of the proposed system using the latest Text REtrieval Conference TREC-8 data was comparable to results reported by the top TREC-8 contenders.