UzNER-Style
Description
This dataset presents a multi-genre named entity recognition (NER) corpus for the Uzbek language. The corpus contains 75,000 manually annotated sentences collected from five functional styles of Uzbek: official, journalistic, scientific, literary, and conversational. Each style includes 15,000 sentences, making the dataset suitable for both in-domain NER evaluation and cross-style robustness analysis. The dataset is annotated using the BIOES tagging scheme and covers a broad set of named entity categories, including persons, organizations, geopolitical entities, dates, money expressions, quantities, durations, percentages, locations, laws, documents, positions, events, products, languages, and other domain-relevant entity types. The inclusion of different Uzbek styles allows researchers to evaluate how NER models perform across diverse lexical, syntactic, and contextual conditions. The corpus is intended to support research on Uzbek natural language processing, low-resource NER, sequence labeling, domain adaptation, and cross-genre model generalization. It can be used for training, validation, testing, and comparative evaluation of machine learning and deep learning models for Uzbek named entity recognition.
Files
Steps to reproduce
The dataset was created by collecting Uzbek texts from five functional styles, cleaning and normalizing the texts, segmenting them into sentences and tokens, and manually annotating named entities using the BIOES tagging scheme. Each style contains 15,000 sentences, resulting in 75,000 annotated sentences in total. The final dataset can be used to reproduce in-domain and cross-style NER experiments for Uzbek.