Título: | UAM at INEX 2012 relevance feedback track: using a probabilistic method for ranking refinement |
Autor(es): | VILLATORO TELLO, ESAU SANCHEZ SANCHEZ, CHRISTIAN JIMENEZ SALAZAR, HECTOR LUNA RAMIREZ, WULFRANO ARTURO RODRIGUEZ LUCATERO, CARLOS |
Temas: | Recuperación de información Método probabilístico Refinamiento de clasificación |
Fecha: | 2012 |
Editorial: | Roma : Initiative for the Evaluation of XML |
Citation: | INEX’12 Workshop Pre-proceeding |
Resumen: | This paper describes the system developed by the Language and Reasoning Group of UAM for the Relevance Feedback track of INEX 2012. The presented system focuses on the problem of ranking documents in accordance to their relevance. It is mainly based on the following hypotheses: (i) current IR machines are able to retrieve relevant documents for most of general queries, but they can not generate a pertinent ranking; and (ii) focused relevance feedback could provide more and better elements for the ranking process than isolated query terms. Based on these hypotheses, our participation at INEX 2012 aimed to demonstrate that using some query-related relevance feedback it is possible to improve the final ranking of the retrieved documents. |
URI: | http://ilitia.cua.uam.mx:8080/jspui/handle/123456789/789 |
Aparece en las colecciones: | Libros |
Fichero | Descripción | Tamaño | Formato | |
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UAM at INEX 2012 Relevance Feedback Track Using a Probabilistic Method for Ranking Refinement.pdf | 4.82 MB | Adobe PDF | Visualizar/Abrir |
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