A Probabilistic Method for Ranking Refinement in Geographic Information Retrieval

Esaú Villatoro-Tello , R. Omar Chavéz-García , Manuel Montes-y-Gómez , Luis Villaseñor-Pineda , L. Enrique Sucar


Recent evaluation results from Geographic Information Retrieval (GIR) indicate that current information retrieval methods are effective to retrieve relevant documents for Geographic queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results in this paper we propose a novel method to re-order the list of documents returned by a GIR system. The proposed method is based on a Markov Random Field (MRF)model that combines the original order obtained by the GIR system, the similarity between documents and a relevance feedback approach, all of them with the purpose of separating relevant from irrelevant documents, and thus, obtaining a more appropriate order. Experiments were conducted with resources from the GeoCLEF forum. Obtained results show the feasibility of the method for re-ranking documents in GIR and also depict an improvement in mean average precision (MAP) when compared to the traditional vector space model

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