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.

翻译：本研究提出了一种基于词性标注（POS）的句子骨架结构提取模型，该模型采用基于BERT架构的迁移学习方法进行词元分类。通过在俄语文本上的微调，模型展现出良好的性能。该方法在增强自然语言处理任务方面具有潜在应用价值，例如可应用于机器翻译的改进。关键词：词性标注，形态分析，自然语言处理，BERT。