Abstract
Problems of determining the author of texts (authorship attribution) or her/his characteristics (authorship profiling) using data mining techniques, although often solved disjointly, in fact are related to idiolect identification and should be studied in unified framework with obligatory account for interaction of as much factors of idiolectal variation (both author-based factors – gender, age, cognitive ability, personality traits, etc. and text-based factors – genre, topic, mode, etc.) as possible. Despite an enormous number of papers proposing rigorous methods of authorship analysis and a high social impact of these tasks, practical applicability of the techniques is questioned. This is because of the underestimation of interaction of the above-mentioned factors of idiolectal variation, which may result, for example, in topic, not author identification, in case of lack of topic control in authorship experiments, and other misleading conclusions. A small number of appropriate corpora also hampers progress in idiolect studies. This paper introduces a new freely available resource RusIdiolect which allows users to search for factors of idiolectal variation related to both author (person, gender, age, etc.) and text (register, mode, genre) as well as to perform full-text search. Database structure is outlined, as well as its possible applications in idiolect studies. The necessity to further develop corpora supplied with information on factors of idiolectal variation to facilitate this research area is highlighted.
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Acknowledgement
The research has been performed in Voronezh State Pedagogical University under the support of Russian Science Foundation, grant number 18-78-10081, which is gratefully acknowledged.
The author expresses her gratitude to Bulat Yaminov and Ildar Yaminov for their invaluable contribution to database design and construction. The author also thanks two anonymous reviewers for their insightful comments.
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Litvinova, T. (2021). RusIdiolect: A New Resource for Authorship Studies. In: Antipova, T. (eds) Comprehensible Science. ICCS 2020. Lecture Notes in Networks and Systems, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-66093-2_2
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DOI: https://doi.org/10.1007/978-3-030-66093-2_2
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