Resumen. En este trabajo se presenta un algoritmo para autocompletar consultas, el cual genera semiautomáticamente términos que el usuario podría emplear para plantear adecuadamente una consulta y aumentar la efectividad de un Sistema de Recuperación de Información. Con el fin de determinar dichas palabras, se utilizan cadenas de Markov, n-gramas y el punto de transición de Goffman. Este método se aplicó a un corpus general construido con textos de Wikipedia y los resultados obtenidos en los experimentos sugirieron la inclusión de palabras importantes en la formulación de consultas, palabras consideradas relevantes de acuerdo con el modelo de espacio vectorial.Abstract. This paper presents an algorithm to autocomplete queries, which semiautomatically generates terms that the user could utilize to properly write a query and increase thereby the effectiveness in an Information Retrieval System. In order to determine these words, Markov chains, n-grams and the Goffman's transition point are used. This method was applied to a general corpus elaborated with texts of Wikipedia and the results obtained in the experiments suggested the inclusion of important words in the query formulation, words considered relevant according to the vector space model.
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