Construction of query concepts based on feature clustering of documents

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 effect...

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Bibliographic Details
Published in:Information retrieval (Boston) 2006-06, Vol.9 (3), p.231-248
Main Authors: YOUJIN CHANG, KIM, Minkoo, RAGHAVAN, Vijay V
Format: Article
Language:English
Subjects:
Clustering
Correlation
Documents
Exact sciences and technology
Feedback
Information and communication sciences
Information retrieval
Information retrieval systems
Information retrieval systems. Information and document management system
Information science. Documentation
Probability
Queries
Query expansion
Relevance feedback
Retrieval performance measures
Sciences and techniques of general use
Studies
Users
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Summary: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. [PUBLICATION ABSTRACT]
ISSN:1386-4564
1573-7659
DOI:10.1007/s10791-006-0837-9