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dc.date.accessioned 2013-11-21T14:21:02Z
dc.date.available 2013-11-21T14:21:02Z
dc.date.issued 2013-10
dc.identifier.uri http://hdl.handle.net/10915/31256
dc.description.abstract 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. en
dc.format.extent 9 p. es
dc.language es es
dc.title A novel, Language-Independent Keyword Extraction method en
dc.type Objeto de conferencia es
sedici.creator.person Aquino, Germán Osvaldo es
sedici.creator.person Hasperué, Waldo es
sedici.creator.person Estrebou, César Armando es
sedici.creator.person Lanzarini, Laura Cristina es
sedici.description.note X Workshop bases de datos y minería de datos es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.keyword text mining en
sedici.subject.keyword document characterization en
sedici.subject.keyword back-propagation en
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.relation.event XVIII Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es
sedici.subject.acmcss98 Data mining es
sedici.subject.acmcss98 DATABASE MANAGEMENT es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)