Authorship Attribution task

Student: Kopysov Mark

Supervisor: Oleg Durandin

Faculty: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

Due to the necessity to structure a plethora of texts that have become available to the public eye and a blazing fast expansion of the Internet, interest in authorship analysis among natural language processing experts has skyrocketed. It has even led to a creation of competitions (such as PAN) with curated datasets, skilled participants and an overview of traditional and non-traditional approaches. An automated, computationally-supported and statistically accurate system of authorship attribution and identification might be of interest to a wide variety of experts. This thesis presents the practical implementation of various methods for text analysis and classification, which allows to solve the multi-domain open-set problem of attribution of the author with sufficient accuracy.

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