THE IMPACT OF ARTIFICIAL INTELLIGENCE IN COMPARATIVE LINGUISTICS: A COMPARATIVE ANALYSIS OF SYNTACTIC AND SEMANTIC ERRORS IN MACHINE TRANSLATION OF TURKIC LANGUAGES
Abstract
This article explores an under examined topic in comparative linguistics: the syntactic and semantic errors in machine translation systems for Turkic languages, focusing on Uzbek and Turkish. Using platforms like Google Translate and Microsoft Translator, the study analyzes translation errors to highlight differences between Central Asian and Anatolian Turkic variants. Grounded in recent advancements in natural language processing (NLP), this research proposes new insights for comparative linguistics and underscores the need for specialized corpora for Turkic languages. The findings aim to contribute to both NLP and linguistic scholarship by identifying AI limitations and suggesting improvements.
Keywords
comparative linguistics, Turkic languages, machine translation, syntactic errors, semantic errors, artificial intelligence, NLP, Uzbek language, Turkish language, transfer learning.
References
- Recent Advancements and Challenges of Turkic Central Asian Languages in the NLP Sphere. arXiv, 2024.
- Systematization of the Teaching of the Turkic Language Vocabulary. ERIC, 2024.
- Turkic-Mongolian Language Parallels in Comparative Historical Perspective. EJAL, 2024.
- Professor Shares Research in Turkish Linguistics. Syracuse University, 2024.
- Challenges in Machine Translation for Agglutinative Languages. Journal of Computational Linguistics, 2023.
- Bibliometric analysis of Asian 'language and linguistics' research. Nature, 2023.
- Improving NLP for Low-Resource Languages: A Case Study of Turkic Variants. ACL, 2024.