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Speaking American: A History of English in the United States

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Language, Literacy, and Technology

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Academic Paper


Title: Document ranking refinement using a Markov random field model
Author: Esaú Villatoro
Institution: National Institute of Astrophysics
Author: Antonio Juárez
Institution: National Institute of Astrophysics
Author: Manuel Montes
Institution: National Institute of Astrophysics
Author: Luis Villaseñor
Institution: National Institute of Astrophysics
Author: Enrique L. Sucar
Institution: National Institute of Astrophysics
Linguistic Field: Computational Linguistics; Text/Corpus Linguistics
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.

CUP AT LINGUIST

This article appears IN Natural Language Engineering Vol. 18, Issue 2, which you can READ on Cambridge's site .



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