An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN Approach
Authorship verification is a crucial process employed to determine the authorship of a given text by analyzing distinct aspects of the writer’s style, such as vocabulary, syntax, and punctuation. This process has gained significant research attention in various domains, including intellec...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10196421/ |
| _version_ | 1797746117169905664 |
|---|---|
| author | Talha Farooq Khan Waheed Anwar Humera Arshad Syed Naseem Abbas |
| author_facet | Talha Farooq Khan Waheed Anwar Humera Arshad Syed Naseem Abbas |
| author_sort | Talha Farooq Khan |
| collection | DOAJ |
| description | Authorship verification is a crucial process employed to determine the authorship of a given text by analyzing distinct aspects of the writer’s style, such as vocabulary, syntax, and punctuation. This process has gained significant research attention in various domains, including intellectual property rights, plagiarism detection, cybercrime investigations, copyright infringement, and forensics. While extensive studies have been conducted on multiple languages worldwide, encompassing Western European languages like Italian and Spanish, as well as Asian languages such as Bengali and Chinese, the investigation of authorship verification in Urdu has been comparatively limited, despite its status as a prominent South Asian language. This limitation can be attributed to the intricate and distinctive morphology of Urdu, which necessitates specific methodologies that cannot be directly applied in the same manner as other languages. To bridge this gap, we propose an innovative approach for authorship verification in Urdu, leveraging Convolutional Neural Networks (CNNs) with three distinct hyper-tuned parameters: ADAM, SGD, and RMSProp. To facilitate the development of this approach, we have curated a new corpus called UAVC-22, specifically tailored for Urdu authorship verification. This corpus offers enhanced robustness in terms of authors’ classes and unique words. We have developed 9 authorship verification models, utilizing three different text embedding techniques, namely Word2Vec, GloVe, and FastText, we have performed a comparative analysis with traditional machine learning models such as Support Vector Machines (SVM) and Random Forest to assess the superiority and efficacy of the CNN-based approach. The optimized CNN-ADAM model with FastText achieved the highest accuracy of 98% for the Urdu dataset UAVC-22. |
| first_indexed | 2024-03-12T15:32:28Z |
| format | Article |
| id | doaj.art-72671f0915b0435c8c692403f3ce40a4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T15:32:28Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-72671f0915b0435c8c692403f3ce40a42023-08-09T23:00:20ZengIEEEIEEE Access2169-35362023-01-0111804038041510.1109/ACCESS.2023.329956510196421An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN ApproachTalha Farooq Khan0https://orcid.org/0000-0002-6230-6406Waheed Anwar1https://orcid.org/0000-0002-2374-6951Humera Arshad2Syed Naseem Abbas3Department of Computer Science, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Computer Science, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Computer Science, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Computer Science, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, PakistanAuthorship verification is a crucial process employed to determine the authorship of a given text by analyzing distinct aspects of the writer’s style, such as vocabulary, syntax, and punctuation. This process has gained significant research attention in various domains, including intellectual property rights, plagiarism detection, cybercrime investigations, copyright infringement, and forensics. While extensive studies have been conducted on multiple languages worldwide, encompassing Western European languages like Italian and Spanish, as well as Asian languages such as Bengali and Chinese, the investigation of authorship verification in Urdu has been comparatively limited, despite its status as a prominent South Asian language. This limitation can be attributed to the intricate and distinctive morphology of Urdu, which necessitates specific methodologies that cannot be directly applied in the same manner as other languages. To bridge this gap, we propose an innovative approach for authorship verification in Urdu, leveraging Convolutional Neural Networks (CNNs) with three distinct hyper-tuned parameters: ADAM, SGD, and RMSProp. To facilitate the development of this approach, we have curated a new corpus called UAVC-22, specifically tailored for Urdu authorship verification. This corpus offers enhanced robustness in terms of authors’ classes and unique words. We have developed 9 authorship verification models, utilizing three different text embedding techniques, namely Word2Vec, GloVe, and FastText, we have performed a comparative analysis with traditional machine learning models such as Support Vector Machines (SVM) and Random Forest to assess the superiority and efficacy of the CNN-based approach. The optimized CNN-ADAM model with FastText achieved the highest accuracy of 98% for the Urdu dataset UAVC-22.https://ieeexplore.ieee.org/document/10196421/Authorship verificationlow resource languagenatural language processingdeep learning |
| spellingShingle | Talha Farooq Khan Waheed Anwar Humera Arshad Syed Naseem Abbas An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN Approach IEEE Access Authorship verification low resource language natural language processing deep learning |
| title | An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN Approach |
| title_full | An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN Approach |
| title_fullStr | An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN Approach |
| title_full_unstemmed | An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN Approach |
| title_short | An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN Approach |
| title_sort | empirical study on authorship verification for low resource language using hyper tuned cnn approach |
| topic | Authorship verification low resource language natural language processing deep learning |
| url | https://ieeexplore.ieee.org/document/10196421/ |
| work_keys_str_mv | AT talhafarooqkhan anempiricalstudyonauthorshipverificationforlowresourcelanguageusinghypertunedcnnapproach AT waheedanwar anempiricalstudyonauthorshipverificationforlowresourcelanguageusinghypertunedcnnapproach AT humeraarshad anempiricalstudyonauthorshipverificationforlowresourcelanguageusinghypertunedcnnapproach AT syednaseemabbas anempiricalstudyonauthorshipverificationforlowresourcelanguageusinghypertunedcnnapproach AT talhafarooqkhan empiricalstudyonauthorshipverificationforlowresourcelanguageusinghypertunedcnnapproach AT waheedanwar empiricalstudyonauthorshipverificationforlowresourcelanguageusinghypertunedcnnapproach AT humeraarshad empiricalstudyonauthorshipverificationforlowresourcelanguageusinghypertunedcnnapproach AT syednaseemabbas empiricalstudyonauthorshipverificationforlowresourcelanguageusinghypertunedcnnapproach |