Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner
<p>Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D)...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , |
| منشور في: |
2020
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513561256001536 |
|---|---|
| author | Md. Tawkat Islam Khondaker (16870107) |
| author2 | Junaed Younus Khan (16870110) Tanvir Alam (638619) M. Sohel Rahman (12056885) |
| author2_role | author author author |
| author_facet | Md. Tawkat Islam Khondaker (16870107) Junaed Younus Khan (16870110) Tanvir Alam (638619) M. Sohel Rahman (12056885) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Tawkat Islam Khondaker (16870107) Junaed Younus Khan (16870110) Tanvir Alam (638619) M. Sohel Rahman (12056885) |
| dc.date.none.fl_str_mv | 2020-09-03T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.3021658 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Agree-to-Disagree_A2D_A_Deep_Learning-Based_Framework_for_Authorship_Discrimination_Task_in_Corpus-Specificity_Free_Manner/24016149 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Machine learning Language, communication and culture Linguistics Task analysis Writing Machine learning Forensics Electronic mail Computer science Natural language processing Authorship discrimination Deep learning Neural networks |
| dc.title.none.fl_str_mv | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel framework for the authorship discrimination task. It is a two-stage deep learning-based framework consisting of an `Agree' and a `Disagree' network. At the first stage, it learns the authorship attributes with its Agree network. Subsequently, through its Disagree network, the framework attempts to differentiate the authorship of a new dataset (completely unrelated to the training dataset), a novel use case that has not been systematically considered hitherto in the literature. We show that A2D is not dependent on the dataset-specific prior knowledge and it can learn only from authorship attributes of the dataset to detect whether two different writings are from the same author. We prove that the A2D framework can successfully reveal the authorship with pseudonyms through tasking it with unfolding the pseudonyms of a famous American short story writer Washington Irving. We also apply our framework on a historical topic of ascertaining whether the authorship of the most respected book in Islam (the Holy Quran) can be attributed to the Prophet of Islam. Through the experimental analysis, A2D reveals that the Prophet of Islam is not the author of the Holy Quran, and this result is in perfect alignment with the belief of 1.8 billion Muslims around the globe regarding the authorship of this holy book.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3021658" target="_blank">https://dx.doi.org/10.1109/access.2020.3021658</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_cb8d7682afe60d5f155f981cbee0d582 |
| identifier_str_mv | 10.1109/access.2020.3021658 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24016149 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free MannerMd. Tawkat Islam Khondaker (16870107)Junaed Younus Khan (16870110)Tanvir Alam (638619)M. Sohel Rahman (12056885)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningLanguage, communication and cultureLinguisticsTask analysisWritingMachine learningForensicsElectronic mailComputer scienceNatural language processingAuthorship discriminationDeep learningNeural networks<p>Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel framework for the authorship discrimination task. It is a two-stage deep learning-based framework consisting of an `Agree' and a `Disagree' network. At the first stage, it learns the authorship attributes with its Agree network. Subsequently, through its Disagree network, the framework attempts to differentiate the authorship of a new dataset (completely unrelated to the training dataset), a novel use case that has not been systematically considered hitherto in the literature. We show that A2D is not dependent on the dataset-specific prior knowledge and it can learn only from authorship attributes of the dataset to detect whether two different writings are from the same author. We prove that the A2D framework can successfully reveal the authorship with pseudonyms through tasking it with unfolding the pseudonyms of a famous American short story writer Washington Irving. We also apply our framework on a historical topic of ascertaining whether the authorship of the most respected book in Islam (the Holy Quran) can be attributed to the Prophet of Islam. Through the experimental analysis, A2D reveals that the Prophet of Islam is not the author of the Holy Quran, and this result is in perfect alignment with the belief of 1.8 billion Muslims around the globe regarding the authorship of this holy book.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3021658" target="_blank">https://dx.doi.org/10.1109/access.2020.3021658</a></p>2020-09-03T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.3021658https://figshare.com/articles/journal_contribution/Agree-to-Disagree_A2D_A_Deep_Learning-Based_Framework_for_Authorship_Discrimination_Task_in_Corpus-Specificity_Free_Manner/24016149CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240161492020-09-03T00:00:00Z |
| spellingShingle | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner Md. Tawkat Islam Khondaker (16870107) Information and computing sciences Artificial intelligence Data management and data science Machine learning Language, communication and culture Linguistics Task analysis Writing Machine learning Forensics Electronic mail Computer science Natural language processing Authorship discrimination Deep learning Neural networks |
| status_str | publishedVersion |
| title | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_full | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_fullStr | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_full_unstemmed | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_short | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_sort | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Language, communication and culture Linguistics Task analysis Writing Machine learning Forensics Electronic mail Computer science Natural language processing Authorship discrimination Deep learning Neural networks |