BBPOS: BERT-based Part-of-Speech Tagging for Uzbek
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915107434397696 |
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| author | Bobojonova, Latofat Akhundjanova, Arofat Ostheimer, Phil Fellenz, Sophie |
| author_facet | Bobojonova, Latofat Akhundjanova, Arofat Ostheimer, Phil Fellenz, Sophie |
| contents | This paper advances NLP research for the low-resource Uzbek language by evaluating two previously untested monolingual Uzbek BERT models on the part-of-speech (POS) tagging task and introducing the first publicly available UPOS-tagged benchmark dataset for Uzbek. Our fine-tuned models achieve 91% average accuracy, outperforming the baseline multi-lingual BERT as well as the rule-based tagger. Notably, these models capture intermediate POS changes through affixes and demonstrate context sensitivity, unlike existing rule-based taggers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10107 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BBPOS: BERT-based Part-of-Speech Tagging for Uzbek Bobojonova, Latofat Akhundjanova, Arofat Ostheimer, Phil Fellenz, Sophie Computation and Language Artificial Intelligence This paper advances NLP research for the low-resource Uzbek language by evaluating two previously untested monolingual Uzbek BERT models on the part-of-speech (POS) tagging task and introducing the first publicly available UPOS-tagged benchmark dataset for Uzbek. Our fine-tuned models achieve 91% average accuracy, outperforming the baseline multi-lingual BERT as well as the rule-based tagger. Notably, these models capture intermediate POS changes through affixes and demonstrate context sensitivity, unlike existing rule-based taggers. |
| title | BBPOS: BERT-based Part-of-Speech Tagging for Uzbek |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.10107 |