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Can Shallow Semantic Class Information Help Answer Passage Retrieval?

Ofoghi, Bahadorreza and Yearwood, John (2009) Can Shallow Semantic Class Information Help Answer Passage Retrieval? In: AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009, Proceedings. Nicholson, Ann and Li, Xiaodong, eds. Lecture Notes in Computer Science , 5866 . Springer, Berlin, New York, pp. 587-596.

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Abstract

In this paper, the effect of using semantic class overlap evidence in enhancing the passage retrieval effectiveness of question answering (QA) systems is tested. The semantic class overlap between questions and passages is measured by evoking FrameNet semantic frames using a shallow term-lookup procedure. We use the semantic class overlap evidence in two ways: i) fusing passage scores obtained from a baseline retrieval system with those obtained from the analysis of semantic class overlap (fusion-based approach), and ii) revising the passage scoring function of the baseline system by incorporating semantic class overlap evidence (revision-based approach). Our experiments with the TREC 2004 and 2006 datasets show that the revision-based approach significantly improves the passage retrieval effectiveness of the baseline system.

Item Type: Book Section
ISBN: 9783642104381 (print) 9783642104398 (online) 0302-9743 (ISSN)
Uncontrolled Keywords: ResPubID22604, computational intelligence, retrieval system, retrieved passages, frame semantics
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
Faculty/School/Research Centre/Department > Institute of Sport, Exercise and Active Living (ISEAL)
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Depositing User: VUIR
Date Deposited: 25 Jan 2014 06:19
Last Modified: 14 Jan 2015 05:37
URI: http://vuir.vu.edu.au/id/eprint/9708
DOI: 10.1007/978-3-642-10439-8_59
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Citations in Scopus: 0 - View on Scopus

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