BBPOS: BERT-based Part-of-Speech Tagging for Uzbek
This work addresses NLP challenges for Uzbek speakers by providing improved tools and resources, though it is incremental as it applies existing methods to a new language.
The paper tackled part-of-speech tagging for the low-resource Uzbek language by fine-tuning monolingual BERT models, achieving 91% average accuracy and outperforming baselines while introducing the first publicly available benchmark dataset.
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.