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Document ranking refinement using a Markov random field model*

Published online by Cambridge University Press:  14 March 2012

ESAÚ VILLATORO ,
ANTONIO JUÁREZ ,
MANUEL MONTES ,
LUIS VILLASEÑOR  and
L. ENRIQUE SUCAR
ESAÚ VILLATORO
Affiliation:
Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro 1 Tonantzintla, Puebla, CP 72840, México e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
ANTONIO JUÁREZ
Affiliation:
Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro 1 Tonantzintla, Puebla, CP 72840, México e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
MANUEL MONTES
Affiliation:
Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro 1 Tonantzintla, Puebla, CP 72840, México e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
LUIS VILLASEÑOR
Affiliation:
Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro 1 Tonantzintla, Puebla, CP 72840, México e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
L. ENRIQUE SUCAR
Affiliation:
Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro 1 Tonantzintla, Puebla, CP 72840, México e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
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Abstract

This paper introduces a novel ranking refinement approach based on relevance feedback for the task of document retrieval. We focus on the problem of ranking refinement since recent evaluation results from Information Retrieval (IR) systems indicate that current methods are effective retrieving most of the relevant documents for different sets of queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results, we propose a novel method to re-rank the list of documents returned by an IR system. The proposed method is based on a Markov Random Field (MRF) model that classifies the retrieved documents as relevant or irrelevant. The proposed MRF combines: (i) information provided by the base IR system, (ii) similarities among documents in the retrieved list, and (iii) relevance feedback information. Thus, the problem of ranking refinement is reduced to that of minimising an energy function that represents a trade-off between document relevance and inter-document similarity. Experiments were conducted using resources from four different tasks of the Cross Language Evaluation Forum (CLEF) forum as well as from one task of the Text Retrieval Conference (TREC) forum. The obtained results show the feasibility of the method for re-ranking documents in IR and also depict an improvement in mean average precision compared to a state of the art retrieval machine.

Type
Articles
Information
Natural Language Engineering , Volume 18 , Special Issue 2: Statistical Learning of Natural Language Structured Input and Output , April 2012 , pp. 155 - 185
Copyright
Copyright © Cambridge University Press 2012

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