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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2411.14393 |
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| _version_ | 1866915290919469056 |
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| author | Churakov, Grigorii |
| author_facet | Churakov, Grigorii |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14393 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | POS-tagging to highlight the skeletal structure of sentences Churakov, Grigorii Computation and Language 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. |
| title | POS-tagging to highlight the skeletal structure of sentences |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2411.14393 |