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Corpus-Based Data Processing of Terminological Systems in Linguistic Theory
Abstract
The article presents a corpus-oriented analysis of the terminological dominants of linguistic theory based on publications of the Odesa Linguistic School, which makes it possible to interpret its scholarly discourse as an institutionally coherent and quantitatively verified body of knowledge. The study is grounded in a combination of corpus-linguistic methods and linguistic data analysis, within which scholarly texts are treated as a structured informational resource. The corpus analysis encompasses the frequency-based parametrization of terms, the identification of their keyness, concordance analysis of usage contexts, modelling of collocational relations, and a diachronic assessment of the dynamics of the terminological system. The totality of these procedures provides a formalized description of the School’s metalanguage and its internal structural organization. The results demonstrate the presence of a stable invariant core of the terminological system, formed by basic and derivative terms of linguistic theory, as well as a dynamic periphery associated with the development of specialized subfields. Of particular analytical value is the subcorpus of suggestive linguistics, which emerges as an autonomous, statistically verified, and methodologically mature segment of the institutional discourse. The applied approach ensures the institutional validation of the Odesa Linguuistic School as an integral scholarly formation with its own metalanguage and a controlled innovative dynamics, and demonstrates the effectiveness of corpus-based and quantitative methods for the analysis of collective scholarly discourses in contemporary humanities research.
Keywords
Data Analysis
Corpus-based Modelling
Quantitative Linguistic Analysis Institutional Discourse
Terminological System
Suggestive Linguistics
Odesa Linguistic School.
References
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Proceedings of the International Conference on Applied Innovations in IT
by
Anhalt University of Applied Sciences
is licensed under
CC BY-SA 4.0
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This work is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License
All works are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), unless otherwise noted.
Published by ICAIIT in cooperation with Anhalt University of Applied Sciences.