Two-stage statistical language models for text database selection |
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Authors: | Hui Yang Minjie Zhang |
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Institution: | (1) School of Information Technology and Computer Science, University of Wollongong, Wollongong, 2500, Australia |
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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. |
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Keywords: | Database language model Text database selection Distributed information retrieval Hierarchical topics Statistical language modeling Query expansion |
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