Adaptive document clustering based on query-based similarity
SCIE
SCOPUS
- Title
- Adaptive document clustering based on query-based similarity
- Authors
- Na, SH; Kang, IS; Lee, JH
- Date Issued
- 2007-07
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Abstract
- 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. (c) 2006 Elsevier Ltd. All rights reserved.
- Keywords
- adaptive document clustering; query-based similarity; cluster-based retrieval; language modeling approach
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/23460
- DOI
- 10.1016/j.ipm.2006.08.008
- ISSN
- 0306-4573
- Article Type
- Article
- Citation
- INFORMATION PROCESSING & MANAGEMENT, vol. 43, no. 4, page. 887 - 901, 2007-07
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.