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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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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. |
format | Online Article Text |
id | pubmed-5038652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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