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From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications

by Nottelmann, Henrik; Fuhr, Norbert

Abstract (Summary)
Information Retrieval systems typically sort the result with respect to document retrieval status values (RSV). According to the Probability Ranking Principle, this ranking ensures optimum retrieval quality if the RSVs are monotonously increasing with the probabilities of relevance (as e.g. for probabilistic IR models). However, advanced applications like filtering or distributed retrieval require estimates of the actual probability of relevance. The relationship between the RSV of a document and its probability of relevance can be described by a 'normalisation' function which maps the retrieval status value onto the probability of relevance ('mapping functions'). In this paper, we explore the use of linear and logistic mapping functions for different retrieval methods. In a series of upper-bound experiments, we compare the approximation quality of the different mapping functions. We also investigate the effect on the resulting retrieval quality in distributed retrieval (only merging, without resource selection). These experiments show that good estimates of the actual probability of relevance can be achieved, and that the logistic model outperforms the linear one. Retrieval quality for distributed retrieval is only slightly improved by using the logistic function.

In:

Information Retrieval 6 (2003), 4

Bibliographical Information:

Advisor:none

School:Universität Duisburg-Essen, Standort Essen

School Location:Germany

Source Type:Master's Thesis

Keywords:informatik datenverarbeitung universitaet duisburg essen

ISBN:

Date of Publication:07/02/2004

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