NOMINALIZATION OF AN ATTRIBUTE IN THE RUSSIAN LANGUAGE

Abstract

This study focuses on the process of nominative representation of attributive semantics in the Russian language, with particular emphasis on color designation. The relevance of the research is determined by the need to comprehend the mechanisms of linguistic representation of complex attributes, especially color shades, in the context of the expanding lexical and semantic needs of modern society.

The aim of the study is to provide an empirical description and theoretical interpretation of the system of analytic nominative units. It also seeks to identify the motivating factors that lead to the activation of analyticism in the Russian language.

The novelty of this research lies in its systematic approach to the study of analytical constructions with color-related attributive semantics, including the analysis of the morpheme-like component «color» as the semantic core and grammatical link within multi-word nominations. The aim of the study is to identify the patterns of formation and functioning of analytical nominations, their correlation with single-word equivalents, and their role in enhancing the expressive capacity of the language.The study applies lexical-semantic, structural, contextual, and comparative methods. The research material includes Russian language dictionaries, literary texts, and data from contemporary Russian language corpora.The findings reveal that analytical color nominations demonstrate a high degree of precision and variability, enabling the expression of complex shades and stylistically marked meanings, thus complementing single-word correlates. The morpheme-like component color functions as both a syntactic and semantic axis in multi-word constructions.

Theoretical significance of the research lies in advancing the understanding of nominative processes and inter-categorical transitions in language. Practical value includes the application of findings in teaching Russian as a native or foreign language, in lexicographic work, and in areas of automated text analysis and generation.