Adaptive document clustering based on query-based similarity

In information retrieval, cluster-based retrieval is a well-known attempt in resolving the problem of term mismatch. Clustering requires similarity information between the documents, which is difficult to calculate at a feasible time. The adaptive document clustering scheme has been investigated by...

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Veröffentlicht in:Information processing & management 2007-07, Vol.43 (4), p.887-901
Hauptverfasser: Na, Seung-Hoon, Kang, In-Su, Lee, Jong-Hyeok
Format: Artikel
Sprache:eng
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Zusammenfassung:In information retrieval, cluster-based retrieval is a well-known attempt in resolving the problem of term mismatch. Clustering requires similarity information between the documents, which is difficult to calculate at a feasible time. The adaptive document clustering scheme has been investigated by researchers to resolve this problem. However, its theoretical viewpoint has not been fully discovered. In this regard, we provide a conceptual viewpoint of the adaptive document clustering based on query-based similarities, by regarding the user’s query as a concept. As a result, adaptive document clustering scheme can be viewed as an approximation of this similarity. Based on this idea, we derive three new query-based similarity measures in language modeling framework, and evaluate them in the context of cluster-based retrieval, comparing with K-means clustering and full document expansion. Evaluation result shows that retrievals based on query-based similarities significantly improve the baseline, while being comparable to other methods. This implies that the newly developed query-based similarities become feasible criterions for adaptive document clustering.
ISSN:0306-4573
1873-5371
DOI:10.1016/j.ipm.2006.08.008