Please use this identifier to cite or link to this item:
https://doi.org/10.1109/ACCESS.2020.2967449
DC Field | Value | |
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dc.title | CAG : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph | |
dc.contributor.author | Sarwar, R. | |
dc.contributor.author | Urailertprasert, N. | |
dc.contributor.author | Vannaboot, N. | |
dc.contributor.author | Yu, C. | |
dc.contributor.author | Rakthanmanon, T. | |
dc.contributor.author | Chuangsuwanich, E. | |
dc.contributor.author | Nutanong, S. | |
dc.date.accessioned | 2021-08-24T02:39:31Z | |
dc.date.available | 2021-08-24T02:39:31Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Sarwar, R., Urailertprasert, N., Vannaboot, N., Yu, C., Rakthanmanon, T., Chuangsuwanich, E., Nutanong, S. (2020). CAG : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph. IEEE Access 8 : 18374-18393. ScholarBank@NUS Repository. https://doi.org/10.1109/ACCESS.2020.2967449 | |
dc.identifier.issn | 2169-3536 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/198973 | |
dc.description.abstract | Stylometry has been successfully applied to perform authorship identification of single-author documents (AISD). The AISD task is concerned with identifying the original author of an anonymous document from a group of candidate authors. However, AISD techniques are not applicable to the authorship identification of multi-author documents (AIMD). Unlike AISD, where each document is written by one single author, AIMD focuses on handling multi-author documents. Due to the combinatoric nature of documents, AIMD lacks the ground truth information - that is, information on writing and non-writing authors in a multi-author document - which makes this problem more challenging to solve. Previous AIMD solutions have a number of limitations: (i) the best stylometry-based AIMD solution has a low accuracy, less than 30%; (ii) increasing the number of co-authors of papers adversely affects the performance of AIMD solutions; and (iii) AIMD solutions were not designed to handle the non-writing authors (NWAs). However, NWAs exist in real-world cases - that is, there are papers for which not every co-author listed has contributed as a writer. This paper proposes an AIMD framework called the Co-Authorship Graph that can be used to (i) capture the stylistic information of each author in a corpus of multi-author documents and (ii) make a multi-label prediction for a multi-author query document. We conducted extensive experimental studies on one synthetic and three real-world corpora. Experimental results show that our proposed framework (i) significantly outperformed competitive techniques; (ii) can effectively handle a larger number of co-authors in comparison with competitive techniques; and (iii) can effectively handle NWAs in multi-author documents. @ 2013 IEEE. | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.source | Scopus OA2020 | |
dc.subject | authorship identification | |
dc.subject | co-authorship graph | |
dc.subject | multi-author documents | |
dc.subject | scientometrics | |
dc.subject | Set similarity search | |
dc.subject | stylometry | |
dc.type | Article | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.1109/ACCESS.2020.2967449 | |
dc.description.sourcetitle | IEEE Access | |
dc.description.volume | 8 | |
dc.description.page | 18374-18393 | |
Appears in Collections: | Staff Publications Elements |
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