DAAB: deep authorship attribution in Bengali
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Islam, Md Saiful
Kayesh, Humayun
Hossain, Md Shafaeat
Anwar, Adnan
Rahman, Khandaker Abir
Razzak, Imran
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Shenzhen, China
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Abstract
Authorship attribution identifies the true author of an unknown document. Authorship attribution plays a crucial role in plagiarism detection and blackmailer identification, however, the existing studies on authorship attribution in Bengali are limited. In this paper, we propose an instance-based deep authorship attribution model, called DAAB, to identify authors in Bengali. Our DAAB model fuses features from convolutional neural networks and another set of features from an artificial neural network to learn the stylometry of an author for authorship attribution. Extensive experiments with three real benchmark datasets such as Bengali-Quora and two online Bengali Corpus demonstrate the superiority of our authorship attribution model.
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2021 International Joint Conference on Neural Networks (IJCNN)
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2021-July
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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Software engineering
Deep learning
Natural language processing
Artificial intelligence not elsewhere classified
Artificial Neural Network
Authorship Attribution
Bengali
Computer Science
Computer Science, Artificial Intelligence
Deep learning
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Plagiarism
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Dipongkor, AK; Islam, MS; Kayesh, H; Hossain, MS; Anwar, A; Rahman, KA; Razzak, I, DAAB: deep authorship attribution in Bengali, 2021 International Joint Conference on Neural Networks (IJCNN), 2021, 2021-July