A Survey on Stylometric Text Features
Year: 2019
Type: conference-paper
Abstract: Ways of individual style expression in a natural language include amongst other things stylometric features. These can be automatically detected with the use of computational linguistics methods. In this survey we systematize the recent studies devoted to extraction and application of stylometric features in solving natural language processing tasks: authorship attribution, authorship verification, style change detection, authorship profiling, and text classification by genre and sentiment. For that purpose we define stylometric feature categories that provide for the most effective solutions, discuss reasons for their successful application, touch upon the limitations of approaches based on their application, and make suggestions for future research.
Authors: Ksenia Lagutina, Nadezhda Lagutina, Elena Boychuk, Inna Vorontsova, Elena Shliakhtina, Olga Belyaeva, Ilya Paramonov, P.G. Demidov
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3004919414