Dynamic Topic Modelling for Semantic Shift Detection

Student: Trofimova Iuliia

Supervisor: Ekaterina Artemova

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

Over time, some words in any languages change their meaning due to semantic shifts. There are approaches to automatically detect such changes, but most of them have been tested in English only. And research for the Russian is almost non-existent. In this paper we will study existing methods for the English language and test them for Russian using dataset about socially and culturally semantic shifts in the period from 2000 to 2014. We want to apply state of the art NLP models for improving quality of detecting semantic shifts in Russian language. Main direction of our research is contextualised word representations.

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