Revisiting logical imaging for information retrieval

Guido Zuccon, Leif Azzopardi, Cornelis J. van Rijsbergen

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

4 Citations (Scopus)

Abstract

Retrieval with Logical Imaging is derived from belief revision and provides a novel mechanism for estimating the relevance of a document through logical implication (i.e. P(q->d). In this poster, we perform the first comprehensive evaluation of Logical Imaging (LI) in Information Retrieval (IR) across several TREC test Collections. When compared against standard baseline models, we show that LI fails to improve performance. This failure can be attributed to a nuance within the model that means non-relevant documents are promoted in the ranking, while relevant documents are demoted. This is an important contribution because it not only contextualizes the effectiveness of LI, but crucially explains why it fails. By addressing this nuance, future LI models could be significantly improved.
Original languageEnglish
Title of host publicationSIGIR '09 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Place of PublicationNew York, NY, USA
Pages766-767
Number of pages2
DOIs
Publication statusPublished - 19 Jul 2009
Externally publishedYes

Keywords

  • logical imaging
  • probability kinematics

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