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Semantic Query Expansion based on Concept Coverage of a Deep Question Category in QA systems  

Kim Hae-Jung (경북대학교 컴퓨터공학과)
Kang Bo-Yeong (한국정보통신대학교 컴퓨터공학과)
Lee Sang-Jo (경북대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Databases / v.32, no.3, 2005 , pp. 297-303 More about this Journal
Abstract
When confronted with a query, question answering systems endeavor to extract the most exact answers possible by determining the answer type that fits with the key terms used in the query. However, the efficacy of such systems is limited by the fact that the terms used in a query may be in a syntactic form different to that of the same words in a document. In this paper, we present an efficient semantic query expansion methodology based on a question category concept list comprised of terms that are semantically close to terms used in a query. The semantically close terms of a term in a query may be hypernyms, synonyms, or terms in a different syntactic category. The proposed system constructs a concept list for each question type and then builds the concept list for each question category using a learning algorithm. In the question answering experiments on 42,654 Wall Street Journal documents of the TREC collection, the traditional system showed in 0.223 in MRR and the proposed system showed 0.50 superior to the traditional question answering system. The results of the present experiments suggest the promise of the proposed method.
Keywords
Question Answering System; Question Category Concept List; Query Expansion;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 J. Prager, D. Radev, and K. Czuba 'Answering What-Is Questions by Virtual Annotation,' In Proceedings of Human Language Technology Conference, pp. 26-30, 2001   DOI
2 Y. Kiyota, S. Kurohashi, and F. Kido, 'Dialog Navigator:A Question Answering System based on Large Text knowledge Base,' In Proceedings of COLING, pp. 460-466. 2002   DOI
3 D. Ravichandran and E. Hovy 'Learning Surface Text Patterns for a Question Answering system,' In Proceedings of the ACL Conference, pp. 41-47, 2002
4 TREC(Text REtrieval Conference) : http://trec.nist.gov/overview.html
5 J. Prager, D. Radev, E. Brown, and A. Coden, 'The Use of Predictive Annotation for Question Answering in TREC8,' In Proceedings of TREC-2000, pp. 399-411, 2000
6 E. Voorhees, 'Query Expansion using Lexical-Semantic Relations,' In Proceedings of the 17th ACM-SIGIR Conference, pp. 61-69, 1994
7 D. Moldovan and R. Mihalcea, 'Using WordNet and Lexical Operators to Improve Internet Searches,' In Proceedings of IEEE Internet Computing, pp. 34-43, 2000   DOI   ScienceOn
8 R. Mandela, T. Tokunage and H. Tanaka, 'Combining Multiple Evidence from Different Types of Thesaurus for Query Expansion,' In Proceedings of the 22nd Annual International ACM SIGIR Conference, pp. 15-19, 1999   DOI
9 C. Cardie, V. Ng, D. Pierce, and C. Buckley, 'Examining the Role of Statistical and Linguistic Knowledge Sources in a General-Knowledge Question-Answering System,' In Proceedings of the 6th ANLP, pp. 180-187, 2000   DOI
10 G. Miller, 'WordNet: A Lexical Database for English,' In Proceedings of the Communications of the ACM, pp. 39-41, 1995   DOI   ScienceOn
11 J. Prager and J. C-Carroll, 'Use of WordNet Hypernyms for Answering What-Is Questions,' In Proceedings of TREC-2001, pp. 143-150, 2000
12 S. Na, I. Kang, O. Kwan, and J. Lee, 'Answer Candidate Ranking based on syntactic Proximity in Question Answering,' In Proceedings of the 29th KISS Sprint Conference, pp. 478-480, 2002   과학기술학회마을
13 R. Mandela, T. Tokunaga, and H. Tanaka, 'Combining Multiple Evidence from Different Types of Thesaurus,' In Proceedings of the 17th ACM-SIGIR Conference, pp. 15-19, 1999
14 M. Pasca and S. Harabagui, 'High Performance Question/Answer,' In Proceedings of the 24rd ACM-SIGIR Conference, pp. 366-374, 2001   DOI
15 J. Kupiec, 'MURAX: A Robust Linguistic Approach for Question Answering Using an On-line Encyclopedia,' In Proceedings of the 16th ACM SIGlR Conference, pp. 181-190, 1993   DOI