A two level learning model for authorship authentication.

Nowadays, forensic authorship authentication plays a vital role in identifying the number of unknown authors as a result of the world's rapidly rising internet use. This paper presents two-level learning techniques for authorship authentication. The learning technique is supplied with linguisti...

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Autores principales:	Ahmed Taha, Heba M Khalil, Tarek El-Shishtawy
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5d3d3afe3d364aae94f29a9017460d30
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spelling	oai:doaj.org-article:5d3d3afe3d364aae94f29a9017460d302021-12-02T20:15:11ZA two level learning model for authorship authentication.1932-620310.1371/journal.pone.0255661https://doaj.org/article/5d3d3afe3d364aae94f29a9017460d302021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255661https://doaj.org/toc/1932-6203Nowadays, forensic authorship authentication plays a vital role in identifying the number of unknown authors as a result of the world's rapidly rising internet use. This paper presents two-level learning techniques for authorship authentication. The learning technique is supplied with linguistic knowledge, statistical features, and vocabulary features to enhance its efficiency instead of learning only. The linguistic knowledge is represented through lexical analysis features such as part of speech. In this study, a two-level classifier has been presented to capture the best predictive performance for identifying authorship. The first classifier is based on vocabulary features that detect the frequency with which each author uses certain words. This classifier's results are fed to the second one which is based on a learning technique. It depends on lexical, statistical and linguistic features. All of the three sets of features describe the author's writing styles in numerical forms. Through this work, many new features are proposed for identifying the author's writing style. Although, the proposed new methodology is tested for Arabic writings, it is general and can be applied to any language. According to the used machine learning models, the experiment carried out shows that the trained two-level classifier achieves an accuracy ranging from 94% to 96.16%.Ahmed TahaHeba M KhalilTarek El-ShishtawyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255661 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ahmed Taha Heba M Khalil Tarek El-Shishtawy A two level learning model for authorship authentication.
description	Nowadays, forensic authorship authentication plays a vital role in identifying the number of unknown authors as a result of the world's rapidly rising internet use. This paper presents two-level learning techniques for authorship authentication. The learning technique is supplied with linguistic knowledge, statistical features, and vocabulary features to enhance its efficiency instead of learning only. The linguistic knowledge is represented through lexical analysis features such as part of speech. In this study, a two-level classifier has been presented to capture the best predictive performance for identifying authorship. The first classifier is based on vocabulary features that detect the frequency with which each author uses certain words. This classifier's results are fed to the second one which is based on a learning technique. It depends on lexical, statistical and linguistic features. All of the three sets of features describe the author's writing styles in numerical forms. Through this work, many new features are proposed for identifying the author's writing style. Although, the proposed new methodology is tested for Arabic writings, it is general and can be applied to any language. According to the used machine learning models, the experiment carried out shows that the trained two-level classifier achieves an accuracy ranging from 94% to 96.16%.
format	article
author	Ahmed Taha Heba M Khalil Tarek El-Shishtawy
author_facet	Ahmed Taha Heba M Khalil Tarek El-Shishtawy
author_sort	Ahmed Taha
title	A two level learning model for authorship authentication.
title_short	A two level learning model for authorship authentication.
title_full	A two level learning model for authorship authentication.
title_fullStr	A two level learning model for authorship authentication.
title_full_unstemmed	A two level learning model for authorship authentication.
title_sort	two level learning model for authorship authentication.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/5d3d3afe3d364aae94f29a9017460d30
work_keys_str_mv	AT ahmedtaha atwolevellearningmodelforauthorshipauthentication AT hebamkhalil atwolevellearningmodelforauthorshipauthentication AT tarekelshishtawy atwolevellearningmodelforauthorshipauthentication AT ahmedtaha twolevellearningmodelforauthorshipauthentication AT hebamkhalil twolevellearningmodelforauthorshipauthentication AT tarekelshishtawy twolevellearningmodelforauthorshipauthentication
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A two level learning model for authorship authentication.

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