Neural network modeling of the semantic field “Internet” in Russian-language discourse

The authors perform the linguistic analysis of neural network modeling of the semantic field “Internet” on the material of available online Russian-language content. The relevance of the study is ensured by the quality and quantity of the linguistic material in the “big data” format and by an innovative methodological approach to its meta-description with neural network instruments. The study is aimed at giving a linguistic characteristic of neural network modeling of the semantic field “Internet” in Russian-language discourse. The material was Russian-language Internet content. The volume of the content had not been limited to obtain statistically representative metadata. This approach corresponds to the mainly declarative limitations of the Internet discourse functionality. Due to the focus on the “intelligent” algorithms for processing Internet content, such as basic for our research OpenAI project, the high referentiality of language data was ensured. The authors used a wide range of methods, from component analysis to discourse analysis, with modern neural network instruments. A two-dimensional neural network modeling was carried out with cluster and stratum analysis of language units associated with the conceptual phenomenon Internet. The conducted research demonstrated the potential of neural network modeling techniques to study the semantic field “Internet”. The modeling identified and verified a wide range of language units whose speech functionality was associated with the conceptual phenomenon Internet as the core of the corresponding semantic field. The results obtained are promising; we can confidently implement the neural network modeling patterns tested in this study into linguistic practice. This, in turn, will develop the paradigm of linguistics, modernize methodological approaches to language functioning, and identify and qualify speech innovations.