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• W352506212 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" @default.
• W352506212 created "2016-06-24" @default.
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• W352506212 date "2013-10-01" @default.
• W352506212 modified "2023-09-23" @default.
• W352506212 title "A novel, Language-Independent Keyword Extraction method" @default.
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