Code Switching Detection in Chinese

Student: Titizian Armine

Supervisor: Ekaterina Artemova

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2021

Learning and processing code switching is one of the most pressing issues of natural language processing. Solving the problem of code switching will improve the quality of translation and supplement parallel corpora construction. Considering the fact that the Chinese language is the most widespread in the world, its correlation with the Russian language is an extremely important task. This research is dedicated to a study of the quality of Chinese word segmetation for sentences with transliterated Russian proper names. The author finetuned language models for token classification task. The best results reported in the research are 94.04 \% recall and 92.9 \% F-score for in-domain data and 81.46 \% F-score and 82.1 \% recall for out-of-domain data.

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