Journal of Siberian Federal University. Humanities & Social Sciences / Features of Linguistic Identification of Language Markers of the Semantic Field “Internet”

Full text (.pdf)
Issue
Journal of Siberian Federal University. Humanities & Social Sciences. 2026 19 (3)
Authors
Barkovich, Aliaksandr; Astapkina, Ekaterina
Contact information
Barkovich, Aliaksandr: Belarusian State University (Minsk, The Republic of Belarus); ; ORCID: 0000-0001-8469-8431; Astapkina, Ekaterina: Belarusian State University (Minsk, The Republic of Belarus); ORCID: 0009-0005-5941-1730
Keywords
semantic field; conceptual phenomenon; language marker; Internet; representation; identification; model
Abstract

The article is devoted to identifying the features of linguistic identification of language markers of the semantic field “Internet”, assessing the potential of its effectiveness and scientifically based adducing of the obtained data and metadata. The relevance of such vector of research is obviously due to the rapid growth in the volume and diversity of Internet content, which inevitably increasingly falls into the focus of linguistic research. The purpose of the study is to characterize the features of linguistic identification of language markers of the semantic field “Internet”. The object is the semantic field “Internet”. The study was carried out using a wide range of linguistic tools, using the procedures of analysis and synthesis, quantitative and qualifying methods. The referential language units identified in the process of studying the semantic field “Internet”, being consistently interpreted, made it possible to fully present the volume and structure of the representation of object semantics. To achieve this goal, the representation of Russian language units related to this semantic continuum was analyzed according to dictionaries, including thesauri; corpora and Internet content. Lexemes associated with the conceptual phenomenon Internet were characterized as language markers of the corresponding semantic field, relevant for its linguistic analysis. Data from dictionary, thesaurus, corpus, and neural network representations were variably generalized into an integrated synthetic representation. The obtained results hold promise as comprehensively tested material, including for supplementation and development in the context of a wide range of scientific research

Pages
618–630
EDN
JNMGKM
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/158221

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