Construction of query concepts based on feature clustering of documents |
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| Authors: | Youjin Chang Minkoo Kim Vijay V Raghavan |
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| Institution: | (1) Graduate School of Information and Communication, Ajou University, Suwon, Korea;(2) Department of Information and Computer Engineering, Ajou University, Suwon, Korea;(3) The Center for Advanced Computer Studies, University of Louisiana, Lafayette, USA |
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| Abstract: | In Information Retrieval, since it is hard to identify users’ information needs, many approaches have been tried to solve
this problem by expanding initial queries and reweighting the terms in the expanded queries using users’ relevance judgments.
Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it
is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach
is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study,
we try to construct query concepts that denote users’ information needs from a document space, rather than to reformulate initial queries using the term correlations
and/or users’ relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form
query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation
shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on
TREC retrieval. |
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| Keywords: | concept-based information retrieval query reformulation query concepts |
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