Resumen: La clasificación de textos, en entornos en los que el volumen de datos a clasificar es tan elevado que resulta muy costosa la realización de esta tarea por parte de humanos, requiere la utilización de clasificadores de textos en lenguaje natural automáticos. El clasificador propuesto en el presente estudio toma como base la Wikipedia para la creación del corpus que define una categoría mediante técnicas de Procesado de Lenguaje Natural (PLN) que analizan sintácticamente los textos a clasificar. El resultado final del sistema propuesto presenta un alto porcentaje de acierto, incluso cuando se compara con los resultados obtenidos con técnicas alternativas de Aprendizaje Automático.Palabras clave: Categorización de textos; Wikipedia; tf-idf; Aprendizaje Automático; Procesado de Lenguaje Natural.Abstract: Automatic Text Classifiers are needed in environments where the amount of data to handle is so high that human classification would be ineffective. In our study, the proposed classifier takes advantage of the Wikipedia to generate the corpus defining each category. The text is then analyzed syntactically using Natural Language Processing software. The proposed classifier is highly accurate and outperforms Machine Learning trained classifiers.

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One of the main tasks of the information services is to help users to find information that satisfies their preferences reducing their search effort. Recommendation systems filter information and only show the most preferred items. Ontologies are fundamental elements of the Semantic Web and have been exploited to build more accurate and personalized recommendations by inferencing missing user preferences. With catalogs changing continuously ontologies must be built autonomously without expert intervention. In this paper we present an audiovisual recommendation engine which uses an enhanced ontology filtering technique to recommend audiovisual content. Experimental results show that the improvements of the ontology filtering technique generate accurate recommendations. In this section we present the scenario where the recommendation engine has been developed, and the ontology filtering technique.