Identification of changes in the semantics of a word in Russian-language news texts using vector models

Investigation of semantic changes is increasingly important for understanding the changes in the Russian language norm that were brought about by the development of the Internet and globalization. A considerable amount of studies has been published on the topic of semantic shifts in the English language (Boukhaled et al., 2019; Hamilton et al., 2016a). However, relatively few studies have attempted to explore semantic changes in Russian (Fomin et al., 2019). Moreover, there is a lack of studies that use programming techniques, such as vector modeling, for examining semantic changes throughout a particular period of time. The aim of the current study is to investigate and categorize semantic changes in Russian news texts dating back to 2000-2023 with the help of vector models. In order to achieve this aim, the study will rely on various methods: computational methods, NLP, semantic and component analysis. The results may show what types of semantic change were the most prevalent in the given period, to what extent they have changed their meaning and how fast those changes became the norm with the help of the Internet environment.