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dc.contributor.advisorAraya-Monge, Josées
dc.contributor.authorAlfaro-Flores, Rafael
dc.date.accessioned2016-10-28T17:58:13Z
dc.date.available2016-10-28T17:58:13Z
dc.date.issued2014
dc.identifier.urihttps://hdl.handle.net/2238/6674
dc.descriptionProyecto de Graduación (Maestría de Ingeniería en Computación con énfasis en Ciencias de la Computación Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Computación, 2014.es
dc.description.abstractAccess to large data, especially for text processing applications, results in more effec tive algorithms and therefore becomes transcendental to take advantage of these large amounts of data. Latent Semantic Analysis (LSA) is an unsupervised machine learning algorithm which benefits from these features and can be used for synonym detection and extraction. LSA takes advantage of the implicit semantic structure that exists in the association between documents and the terms they contain to statistically analyze the relationships between the terms of the collection of text documents; and because it uses a strictly mathematical approach, it is inherently independent of language. This is a thesis for the Masters in Computing degree that analyzes the LSA algorithm in a distributed environment, in order to evaluate its effect for synonym detection and extraction on larger collections of data.es
dc.description.sponsorshipInstituto Tecnológico de Costa Rica.es
dc.language.isospaes
dc.publisherInstituto Tecnológico de Costa Ricaes
dc.subjectAlgoritmoses
dc.subjectDatoses
dc.subjectAprendizajees
dc.subjectAnálisis semántico latentees
dc.subjectEstadísticaes
dc.titleEvaluación del efecto en el algoritmo de Análisis Semántico Latente al utilizar colecciones de datos cada vez más grandes para la detección y extracción de sinónimos y su independencia respecto al lenguaje, por medio de su implementación distribuidaes
dc.typeinfo:eu-repo/semantics/masterThesises


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