This study presents the development of a part-of-speech (POS) tagging model to extract the skeletal structure of sentences using transfer learning with the BERT architecture for token classification. The model, fine-tuned on Russian text, demonstrating its effectiveness. The approach offers potential applications in enhancing natural language processing tasks, such as improving machine translation. Keywords: part of speech tagging, morphological analysis, natural language processing, BERT.
@article{arxiv.2411.14393,
title = {POS-tagging to highlight the skeletal structure of sentences},
author = {Grigorii Churakov},
journal= {arXiv preprint arXiv:2411.14393},
year = {2025}
}
Comments
in Russian language. Conference: Automated control systems and information technologies https://asuit.pstu.ru/ Section: IT and automated systems