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

2025-01-17

AI Summary

Uzbek, a low-resource, morphologically rich language, lacks publicly available universal part-of-speech (UPOS) annotation resources and benchmark datasets for POS tagging. Method: We construct the first open UPOS-annotated benchmark dataset for Uzbek following Universal Dependencies guidelines, fine-tune two monolingual Uzbek BERT models on this data, and systematically evaluate their performance against multilingual BERT and a rule-based tagger. Contribution/Results: Fine-tuned monolingual Uzbek BERT achieves an average accuracy of 91%, substantially outperforming all baselines. This work provides the first empirical validation that monolingual pretraining effectively captures suffix-driven POS variation and context-sensitive morphology—capabilities beyond the reach of traditional rule-based systems. It establishes the first publicly available UPOS benchmark for Uzbek, fills a critical gap in Uzbek NLP infrastructure, and offers a reproducible evaluation framework and effective methodology for POS tagging in low-resource, morphologically complex languages.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Evaluation and Analysis
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Uzbek language
• Part-of-speech tagging
• Accuracy improvement

Innovation

• Uzbek BERT Models
• Part-of-Speech Tagging
• Contextual Adaptation