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

Latofat Bobojonova; Arofat Akhundjanova; Phil Ostheimer; Sophie Fellenz

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arXiv · 2501.10107

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

Latofat Bobojonova·Arofat Akhundjanova·Phil Ostheimer·Sophie Fellenz

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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.

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Latofat Bobojonova, Arofat Akhundjanova, Phil Ostheimer, Sophie Fellenz. 2025-01-17. BBPOS: BERT-based Part-of-Speech Tagging for Uzbek. https://arxiv.org/abs/2501.10107

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