PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Information-Based Models for Ad Hoc IR
Stephane Clinchant and Eric Gaussier
In: SIGIR 2010, 19-23 July 2010, Geneva, Swizterland.

Abstract

We introduce in this paper the family of information-based models for ad hoc information retrieval. These models draw their inspiration from a long-standing hypothesis in IR, name- ly the fact that the difference in the behaviors of a word at the document and collection levels brings information on the significance of the word for the document. This hypothesis has been exploited in the 2-Poisson mixture models, in the notion of eliteness in BM25, and more recently in DFR mod- els. We show here that, combined with notions related to burstiness, it can lead to simpler and better models.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:7028
Deposited By:Eric Gaussier
Deposited On:02 December 2010