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Keyword identification in spanish documents using neural networks

By:

Aquino, Germán

Contributor(s):

Lanzarini, Laura Cristina

Material type: Article

ArticleDescription: 1 archivo (575,1 kB)Subject(s):

Summary: The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present an algorithm for keyword extraction from documents written in Spanish.This algorithm combines autoencoders, which are adequate for highly unbalanced classification problems, with the discriminative power of conventional binary classifiers. In order to improve its performance on larger and more diverse datasets, our algorithm trains several models of each kind through bagging.

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Item type	Home library	Collection	Call number	URL	Status	Date due	Barcode
Capítulo de libro	Biblioteca de la Facultad de Informática	Biblioteca digital	A0787 (Browse shelf(Opens below))	Link to resource	Recurso en Línea

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Previous	A0784 Diseñando transformaciones de modelos CIM / PIM : desde un enfoque de negocio hacia un enfoque de sistema	A0785 A rule-based approach to address semantic accuracy problems on Linked Data	A0786 Keyword extracting using auto-associative neural networks	A0787 Keyword identification in spanish documents using neural networks	A0788 e-News v1.0	A0789 EPRA : herramienta para la enseñanza de conceptos básicos de programación utilizando realidad aumentada	A0791 Language Extended Lexicon points : estimating the size of an application using its language	Next

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The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present an algorithm for keyword extraction from documents written in Spanish.This algorithm combines autoencoders, which are adequate for highly unbalanced classification problems, with the discriminative power of conventional binary classifiers. In order to improve its performance on larger and more diverse datasets, our algorithm trains several models of each kind through bagging.

Congreso Argentino de Ciencias de la Computación (21ro : 2015 : Junín, Argentina)