Keyword extracting using auto-associative neural networks

Autores
Aquino, Germán Osvaldo; Hasperué, Waldo; Lanzarini, Laura Cristina
Año de publicación
2014
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
Versión publicada
Descripción
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 a new algorithm for keyword extraction. Its main goal is to extract keywords from text documents written in Spanish quickly and without requiring a large training set. This goal was achieved using auto-associative neural networks, also known as autoencoders, trained using only the terms designated as keywords in the training set, so that these networks can learn the features characterizing the important terms in a document.
XI Workshop Bases de Datos y Minería de Datos
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
keyword extraction
text mining
neural networks
autoencoders
Nivel de accesibilidad
Acceso abierto
Licencia
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/42284