Semantic query expansion based on a question category concept list

Authors:
Hae-Jung Kim;Bo-Yeong Kang;Seong-Bae Park;Sang-Jo Lee
Affiliations:
Department of Computer Engineering, Kyoungpook National University, Daegu, Korea;Department of Computer Engineering, Kyoungpook National University, Daegu, Korea;Department of Computer Engineering, Kyoungpook National University, Daegu, Korea;Department of Computer Engineering, Kyoungpook National University, Daegu, Korea
Venue:
ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
Year:
2004

Citing 4
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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 query and 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 first constructs a concept list for each question type and then builds the concept list for each question category using a learning algorithm. When a new query is given, the question is classified into the node in question category, and the query is expanded using the concept list of the classified category. 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.