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Título: A novel, Language-Independent Keyword Extraction method
Autores: Aquino, Germán
Hasperué, Waldo
Estrebou, César
Lanzarini, Laura Cristina
Fecha: 2013-11-21
2013-10
Publicador: Unversidad Nacional de La Plata
Fuente:

Tipo: Objeto de conferencia
Objeto de conferencia
Tema: text mining
document characterization
back-propagation
Data mining
DATABASE MANAGEMENT
Ciencias Informáticas
Descripción: Obtaining the most representative set of words in a document is a very significant task, since it allows characterizing the document and simplifies search and classification activities. This paper presents a novel method, called LIKE, that offers the ability of automatically extracting keywords from a document regardless of the language used in it. To do so, it uses a three-stage process: the first stage identifies the most representative terms, the second stage builds a numeric representation that is appropriate for those terms, and the third one uses a feed-forward neural network to obtain a predictive model. To measure the efficacy of the LIKE method, the articles published by the Workshop of Computer Science Researchers (WICC) in the last 14 years (1999-2012) were used. The results obtained show that LIKE is better than the KEA method, which is one of the most widely mentioned solutions in literature about this topic.
X Workshop bases de datos y minería de datos
Idioma: Español