Luis Villaseñor-Pineda

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This paper focuses on the task of bilingual clustering, which involves dividing a set of documents from two different languages into a set of thematically homogeneous groups. It mainly proposes a translation independent approach specially suited to deal with linguistically related languages. In particular, it proposes representing the documents by pairs of(More)
Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as analyzing Web usage, examining purchase behavior, and text mining, among others. Nevertheless, with the dramatic increase in data volume, the current approaches prove inefficient when dealing with large input datasets, a large number of different symbols and(More)
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(More)
Fernando Sánchez-Vega1 Esaú Villatoro-Tello1 Antonio Juárez-Gozález1 Luis Villaseñor-Pineda1 Manuel Montes-y-Gómez1,2 Luis Meneses-Lerín3 (1) Laboratory of Language Technologies, Department of Computational Sciences, National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico. (2) Department of Computer and Information Sciences, University of(More)
This paper describes a language independent method for speaker identification. This method is based on a novel characterization of the speech signal that captures the dynamic information contained in the cepstral coefficients. The proposed method was evaluated through several experiments on a corpus of Mexican speakers. The achieved results demonstrated the(More)
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