Multi-view learning for hierarchical topic detection on corpus of documents

dc.contributorNiño Vasquez, Luis Fernando
dc.contributorLABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI
dc.creatorCalero Espinosa, Juan Camilo
dc.date.accessioned2021-05-26T16:54:28Z
dc.date.available2021-05-26T16:54:28Z
dc.date.created2021-05-26T16:54:28Z
dc.date.issued2021
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/79567
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.description.abstractTopic detection on a large corpus of documents requires a considerable amount of computational resources, and the number of topics increases the burden as well. However, even a large number of topics might not be as specific as desired, or simply the topic quality starts decreasing after a certain number. To overcome these obstacles, we propose a new methodology for hierarchical topic detection, which uses multi-view clustering to link different topic models extracted from document named entities and part of speech tags. Results on three different datasets evince that the methodology decreases the memory cost of topic detection, improves topic quality and allows the detection of more topics.
dc.description.abstractLa detección de temas en grandes colecciones de documentos requiere una considerable cantidad de recursos computacionales, y el número de temas también puede aumentar la carga computacional. Incluso con un elevado nùmero de temas, estos pueden no ser tan específicos como se desea, o simplemente la calidad de los temas comienza a disminuir después de cierto número. Para superar estos obstáculos, proponemos una nueva metodología para la detección jerárquica de temas, que utiliza agrupamiento multi-vista para vincular diferentes modelos de temas extraídos de las partes del discurso y de las entidades nombradas de los documentos. Los resultados en tres conjuntos de documentos muestran que la metodología disminuye el costo en memoria de la detección de temas, permitiendo detectar màs temas y al mismo tiempo mejorar su calidad.
dc.languageeng
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisherDepartamento de Ingeniería de Sistemas e Industrial
dc.publisherFacultad de Ingeniería
dc.publisherBogotá
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.rightsReconocimiento 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleMulti-view learning for hierarchical topic detection on corpus of documents
dc.typeTrabajo de grado - Maestría


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