A novel, Language-Independent Keyword Extraction method

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...

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Detalles Bibliográficos
Autores principales: Aquino, Germán Osvaldo, Hasperué, Waldo, Estrebou, César Armando, Lanzarini, Laura Cristina
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 2013
Materias:
Ciencias Informáticas
Data mining
DATABASE MANAGEMENT
text mining
document characterization
back-propagation
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/31256
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-31256
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
Data mining
DATABASE MANAGEMENT
text mining
document characterization
back-propagation
spellingShingle Ciencias Informáticas
Data mining
DATABASE MANAGEMENT
text mining
document characterization
back-propagation
Aquino, Germán Osvaldo
Hasperué, Waldo
Estrebou, César Armando
Lanzarini, Laura Cristina
A novel, Language-Independent Keyword Extraction method
topic_facet Ciencias Informáticas
Data mining
DATABASE MANAGEMENT
text mining
document characterization
back-propagation
description 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.
format Objeto de conferencia
Objeto de conferencia
author Aquino, Germán Osvaldo
Hasperué, Waldo
Estrebou, César Armando
Lanzarini, Laura Cristina
author_facet Aquino, Germán Osvaldo
Hasperué, Waldo
Estrebou, César Armando
Lanzarini, Laura Cristina
author_sort Aquino, Germán Osvaldo
title A novel, Language-Independent Keyword Extraction method
title_short A novel, Language-Independent Keyword Extraction method
title_full A novel, Language-Independent Keyword Extraction method
title_fullStr A novel, Language-Independent Keyword Extraction method
title_full_unstemmed A novel, Language-Independent Keyword Extraction method
title_sort novel, language-independent keyword extraction method
publishDate 2013
url http://sedici.unlp.edu.ar/handle/10915/31256
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