As the number of treebanks within the same language family continues to grow, the importance of establishing consistent annotation practices has become increasingly evident. In this paper, we evaluate various approaches to annotating Turkic postverbial constructions across UD treebanks. Our comparative analysis reveals that none of the existing methods fully capture the unique semantic and syntactic characteristics of these complex constructions. This underscores the need to adopt a balanced approach that can achieve broad consensus and be implemented consistently across Turkic treebanks. By examining the phenomenon and the available annotation strategies, our study aims to improve the consistency of Turkic UD treebanks and enhance their utility for cross-linguistic research.

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.