Author Identification using Stylometric Features

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Autore principale: Daniel Pavelec
Natura: Artículo científico
Lingua:en
Pubblicazione: Asociación Española para la Inteligencia Artificial 2007
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author Daniel Pavelec
author_facet Daniel Pavelec
contents Author Identification using Stylometric Features Daniel Pavelec Edson Justino Luiz S. Oliveira Ingeniería Stylometry Pattern Recognition Author Identification In this work we present a strategy for author identification for documents written in Portuguese. It takes intoaccount a writer-independent model which reduces the pattern recognition problem to a single model andtwo classes, hence, makes it possible to build robust system even when few genuine samples per writer areavailable. We also introduce a stylometric feature set, which is based on the conjunctions of the Portugueselanguage. Experiments on a database composed of short articles from 10 different authors and Support VectorMachine (SVM) as classifier demonstrate that the proposed strategy can produced results comparable to theliterature 2007 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503609 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.36 Vol.11
format Artículo científico
id redalyc_92503609
institution Redalyc
language en
publishDate 2007
publisher Asociación Española para la Inteligencia Artificial
spellingShingle Author Identification using Stylometric Features
Daniel Pavelec
Ingeniería
Stylometry
Pattern Recognition
Author Identification
Author Identification using Stylometric Features Daniel Pavelec Edson Justino Luiz S. Oliveira Ingeniería Stylometry Pattern Recognition Author Identification In this work we present a strategy for author identification for documents written in Portuguese. It takes intoaccount a writer-independent model which reduces the pattern recognition problem to a single model andtwo classes, hence, makes it possible to build robust system even when few genuine samples per writer areavailable. We also introduce a stylometric feature set, which is based on the conjunctions of the Portugueselanguage. Experiments on a database composed of short articles from 10 different authors and Support VectorMachine (SVM) as classifier demonstrate that the proposed strategy can produced results comparable to theliterature 2007 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503609 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.36 Vol.11
title Author Identification using Stylometric Features
topic Ingeniería
Stylometry
Pattern Recognition
Author Identification
url https://www.redalyc.org/articulo.oa?id=92503609