A Probabilistic Method for Ranking Refinement in Geographic Information Retrieval

• Autores: Esaú Villatoro-Tello, R. Omar Chavéz-García, Manuel Montes y Gómez, Luis Villaseñor Pineda, Enrique Sucar
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 44, 2010, págs. 123-130
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen
  • español

    Resultados recientes en la tarea de Recuperación de Información Geográfica (GIR) indican que los métodos de recuperación de información actuales son efectivos para recuperar documentos relevantes a las consultas geográficas, sin embargo tienen serias dificultades para generar un orden apropiado con los documentos recuperados. Motivado por estos resultados, este trabajo propone un método novedoso para re-ordenar la lista de documentos recuperados por un sistema GIR. El método propuesto está basado en un Campo Aleatorio de Markov (CAM), el cual combina el orden original obtenido por el sistema GIR, la similitud entre documentos, y un enfoque de retroalimentación de relevancia. La combinación de éstas características tiene el propósito de separar los documentos relevantes de los que no lo son y así obtener un orden más apropiado. Se realizaron experimentos con los recursos del foro GeoCLEF. Los resultados obtenidos muestran la viabilidad del método para re-ordenar documentos geográficos y también muestran una mejora en la medida MAP (Mean Average Precision) comparados con el modelo tradicional de espacio vectorial.

  • English

    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 among 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) compared to the traditional vector space model.

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