BBPOS: BERT-based Part-of-Speech Tagging for Uzbek

Latofat Bobojonova, Arofat Akhundjanova, Phil Sidney Ostheimer, Sophie Fellenz


Abstract
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.
Anthology ID:
2025.loreslm-1.23
Volume:
Proceedings of the First Workshop on Language Models for Low-Resource Languages
Month:
January
Year:
2025
Address:
Abu Dhabi, United Arab Emirates
Editors:
Hansi Hettiarachchi, Tharindu Ranasinghe, Paul Rayson, Ruslan Mitkov, Mohamed Gaber, Damith Premasiri, Fiona Anting Tan, Lasitha Uyangodage
Venues:
LoResLM | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
287–293
Language:
URL:
https://aclanthology.org/2025.loreslm-1.23/
DOI:
Bibkey:
Cite (ACL):
Latofat Bobojonova, Arofat Akhundjanova, Phil Sidney Ostheimer, and Sophie Fellenz. 2025. BBPOS: BERT-based Part-of-Speech Tagging for Uzbek. In Proceedings of the First Workshop on Language Models for Low-Resource Languages, pages 287–293, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
BBPOS: BERT-based Part-of-Speech Tagging for Uzbek (Bobojonova et al., LoResLM 2025)
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PDF:
https://aclanthology.org/2025.loreslm-1.23.pdf
Optionalsupplementarymaterial:
 2025.loreslm-1.23.OptionalSupplementaryMaterial.zip