NDLI: A multi-collection latent topic model for federated search

Content Provider	Springer Nature Link
Author	Baillie, Mark Carman, Mark Crestani, Fabio
Copyright Year	2010
Abstract	Collection selection is a crucial function, central to the effectiveness and efficiency of a federated information retrieval system. A variety of solutions have been proposed for collection selection adapting proven techniques used in centralised retrieval. This paper defines a new approach to collection selection that models the topical distribution in each collection. We describe an extended version of latent Dirichlet allocation that uses a hierarchical hyperprior to enable the different topical distributions found in each collection to be modelled. Under the model, resources are ranked based on the topical relationship between query and collection. By modelling collections in a low dimensional topic space, we can implicitly smooth their term-based characterisation with appropriate terms from topically related samples, thereby dealing with the problem of missing vocabulary within the samples. An important advantage of adopting this hierarchical model over current approaches is that the model generalises well to unseen documents given small samples of each collection. The latent structure of each collection can therefore be estimated well despite imperfect information for each collection such as sampled documents obtained through query-based sampling. Experiments demonstrate that this new, fully integrated topical model is more robust than current state of the art collection selection algorithms.
Starting Page	390
Ending Page	412
Page Count	23
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	14
Issue Number	4
e-ISSN	15737659
Language	English
Publisher	Springer Netherlands
Publisher Date	2010-10-07
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Distributed information retrieval Topic models Retrieval Collection selection Information Storage and Retrieval Data Structures, Cryptology and Information Theory Pattern Recognition Data Mining and Knowledge Discovery Document Preparation and Text Processing
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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