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Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs

We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from short...

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Detalles Bibliográficos
Autores principales: Gómez-Adorno, Helena, Sidorov, Grigori, Pinto, David, Vilariño, Darnes, Gelbukh, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038652/
https://www.ncbi.nlm.nih.gov/pubmed/27589740
http://dx.doi.org/10.3390/s16091374
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author Gómez-Adorno, Helena
Sidorov, Grigori
Pinto, David
Vilariño, Darnes
Gelbukh, Alexander
author_facet Gómez-Adorno, Helena
Sidorov, Grigori
Pinto, David
Vilariño, Darnes
Gelbukh, Alexander
author_sort Gómez-Adorno, Helena
collection PubMed
description We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution.
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spelling pubmed-50386522016-09-29 Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs Gómez-Adorno, Helena Sidorov, Grigori Pinto, David Vilariño, Darnes Gelbukh, Alexander Sensors (Basel) Article We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution. MDPI 2016-08-29 /pmc/articles/PMC5038652/ /pubmed/27589740 http://dx.doi.org/10.3390/s16091374 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gómez-Adorno, Helena
Sidorov, Grigori
Pinto, David
Vilariño, Darnes
Gelbukh, Alexander
Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs
title Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs
title_full Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs
title_fullStr Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs
title_full_unstemmed Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs
title_short Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs
title_sort automatic authorship detection using textual patterns extracted from integrated syntactic graphs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038652/
https://www.ncbi.nlm.nih.gov/pubmed/27589740
http://dx.doi.org/10.3390/s16091374
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