Abstract
During the last years a great number of Query Performance Prediction methods have been proposed. However, this explosion of prediction method proposals have not been paralleled by an in-depth study of suitable methods to evaluate these estimations. In this paper we analyse the current approaches to evaluate Query Performance Prediction methods, highlighting some limitations they present. We also propose a novel method for evaluating predictors focused on revealing the different performance they have for queries of distinct degree of difficulty. This goal can be achieved by transforming the prediction performance evaluation problem into a classification task, assuming that each topic belongs to a unique type based on their retrieval performance. We compare the different evaluation approaches showing that the proposed evaluation exhibits a more accurate performance, making explicit the differences between predictors for different types of queries.
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This paper has been funded in part by the Spanish MICINN projects NoHNES (Spanish Ministerio de Educación y Ciencia - TIN2007-68083) and by MAVIR, a research network co-funded by the Regional Government of Madrid under program MA2VICMR (S2009/TIC-1542).
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Pérez-Iglesias, J., Araujo, L. (2010). Evaluation of Query Performance Prediction Methods by Range. In: Chavez, E., Lonardi, S. (eds) String Processing and Information Retrieval. SPIRE 2010. Lecture Notes in Computer Science, vol 6393. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16321-0_23
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DOI: https://doi.org/10.1007/978-3-642-16321-0_23
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