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.
POS-tagging to highlight the skeletal structure of sentences
Transfer learning with the BERT architecture for token classification effectively develops a part-of-speech tagging model for Russian text, enhancing natural language processing tasks like machine translation.
- Year
- 2024
- Venue
- arXiv 2024
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- 1
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- arxiv.org/abs/2411.14393ARXIV-DEFAULT
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