NDLI: A study of the integration of passage-, document-, and cluster-based information for re-ranking search results

Content Provider	Springer Nature Link
Author	Krikon, Eyal Kurland, Oren
Copyright Year	2011
Abstract	Cluster-based and passage-based document retrieval paradigms were shown to be effective. While the former are based on utilizing query-related corpus context manifested in clusters of similar documents, the latter address the fact that a document can be relevant even if only a very small part of it contains query-pertaining information. Hence, cluster-based approaches could be viewed as based on “expanding” the document representation, while passage-based approaches can be thought of as utilizing a “contracted” document representation. We present a study of the relative benefits of using each of these two approaches, and of the potential merits of their integration. To that end, we devise two methods that integrate whole-document-based, cluster-based and passage-based information. The methods are applied for the re-ranking task, that is, re-ordering documents in an initially retrieved list so as to improve precision at the very top ranks. Extensive empirical evaluation attests to the potential merits of integrating these information types. Specifically, the resultant performance substantially transcends that of the initial ranking; and, is often better than that of a state-of-the-art pseudo-feedback-based query expansion approach.
Starting Page	593
Ending Page	616
Page Count	24
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	14
Issue Number	6
e-ISSN	15737659
Language	English
Publisher	Springer Netherlands
Publisher Date	2011-05-20
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Ad hoc retrieval Re-ranking Clusters Cluster-based language models Passages Passage-based language models Data Structures, Cryptology and Information Theory Data Mining and Knowledge Discovery Pattern Recognition Document Preparation and Text Processing Information Storage and Retrieval
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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