The Composition Index in Kartvelian Languages

Abstract

The present work aims to provide a complex study of the morphological systems of the Kartvelian languages through the lens of quantitative typology. The object of research is the morphological structure of the Kartvelian linguistic group (Georgian, Megrelian, Laz, and Svan), specifically focusing on the mechanisms of word formation (composition), the frequency of their functioning, and their place within the overall naming system. Linguistic typology, as the systematic study of linguistic diversity, has evolved beyond purely descriptive frameworks and is increasingly grounded in empirical data and mathematical models. The theoretical and methodological foundation of this paper is the quantitative approach developed by Joseph Greenberg, which allows us to define the morphological character of a language through specific indices. The primary focus of this study is the Index of Composition, which objectively reflects the degree of prevalence of compound words (composites) in a language. The research object is analyzed from two perspectives: the synchronic aspect compares 300-word text samples from the Megrelian, Laz, and Svan languages to determine the extent of similarity regarding composition, despite the fact that structurally some are more agglutinative while others are more inflectional, and the diachronic aspect uses the Georgian literary language as a case study from the 5th to the 21st century to investigate the stability of the morphological framework. The analysis encompasses texts from various eras and functional styles, including hagiography, epics, publicistics, and media texts, allowing us to observe how trends in the use of compounds have changed or persisted over fifteen centuries. Attention is also given to the theoretical visions of Wilhelm von Humboldt, Edward Sapir, and William Croft, which aids in the critical reappraisal of the Eurocentric paradigms characteristic of 19th-century typology. To process the data, the R programming platform (R-Studio) was selected, as this tool enables the processing of statistical and linguistic data with mathematical precision, ensuring the attainment of reliable results.