Yang, H. and Zhang, M. (2006) Two-stage statistical language models for text database selection. Information Retrieval, 9 (1). pp. 5-31. ISSN 1386-4564
Abstract
As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Science + Business Media, Inc. 2006 |
Keywords: | Database language model; Text database selection; Distributed information retrieval; Hierarchical topics; Statistical language modeling; Query expansion |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Jun 2017 10:44 |
Last Modified: | 13 Jun 2017 10:44 |
Published Version: | http://doi.org/10.1007/s10791-005-5719-z |
Status: | Published |
Publisher: | Springer Verlag |
Refereed: | Yes |
Identification Number: | 10.1007/s10791-005-5719-z |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:108940 |