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 linguistic kno...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341647/?tool=EBI |
| _version_ | 1818356315193344000 |
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| author | Ahmed Taha Heba M. Khalil Tarek El-shishtawy |
| author_facet | Ahmed Taha Heba M. Khalil Tarek El-shishtawy |
| author_sort | Ahmed Taha |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-12-13T19:55:15Z |
| format | Article |
| id | doaj.art-b97e36d3593a465683b5febab36c892e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T19:55:15Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b97e36d3593a465683b5febab36c892e2022-12-21T23:33:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01168A two level learning model for authorship authenticationAhmed TahaHeba M. KhalilTarek El-shishtawyNowadays, 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%.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341647/?tool=EBI |
| spellingShingle | Ahmed Taha Heba M. Khalil Tarek El-shishtawy A two level learning model for authorship authentication PLoS ONE |
| title | 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_short | A two level learning model for authorship authentication |
| title_sort | two level learning model for authorship authentication |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341647/?tool=EBI |
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