# YoNER: A New Yor`ub'a Multi-domain Named Entity Recognition Dataset

arXiv: https://arxiv.org/abs/2604.05624 · HTML: https://mlsanity.com/paper/2604.05624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters](https://mlsanity.com/paper/2012.15682/) (2020)
- [Token Dropping for Efficient BERT Pretraining](https://mlsanity.com/paper/2203.13240/) (2022)
- [Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twi](https://mlsanity.com/paper/1912.02481/) (2019)
- [Many-to-English Machine Translation Tools, Data, and Pretrained Models](https://mlsanity.com/paper/2104.00290/) (2021)
- [MasakhaNEWS: News Topic Classification for African languages](https://mlsanity.com/paper/2304.09972/) (2023)
- [Is Multilingual BERT Fluent in Language Generation?](https://mlsanity.com/paper/1910.03806/) (2019)
- [MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages](https://mlsanity.com/paper/2305.13989/) (2023)
- [WikiBERT models: deep transfer learning for many languages](https://mlsanity.com/paper/2006.01538/) (2020)
- [NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification](https://mlsanity.com/paper/2305.10971/) (2023)
- [NaijaNER : Comprehensive Named Entity Recognition for 5 Nigerian Languages](https://mlsanity.com/paper/2105.00810/) (2021)
- [Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification](https://mlsanity.com/paper/2312.07961/) (2023)
- [OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages](https://mlsanity.com/paper/2412.09587/) (2024)

## Abstract (arXiv)

Named Entity Recognition (NER) is a foundational NLP task, yet research in Yor`ub'a has been constrained by limited and domain-specific resources. Existing resources, such as MasakhaNER (a manually annotated news-domain corpus) and WikiAnn (automatically created from Wikipedia), are valuable but restricted in domain coverage. To address this gap, we present YoNER, a new multidomain Yor`ub'a NER dataset that extends entity coverage beyond news and Wikipedia. The dataset comprises about 5,000 sentences and 100,000 tokens collected from five domains including Bible, Blogs, Movies, Radio broadcast and Wikipedia, and annotated with three entity types: Person (PER), Organization (ORG) and Location (LOC), following CoNLL-style guidelines. Annotation was conducted manually by three native Yor`ub'a speakers, with an inter-annotator agreement of over 0.70, ensuring high quality and consistency. We benchmark several transformer encoder models using cross-domain experiments with MasakhaNER 2.0, and we also assess the effect of few-shot in-domain data using YoNER and cross-lingual setups with English datasets. Our results show that African-centric models outperform general multilingual models for Yor`ub'a, but cross-domain performance drops substantially, particularly for blogs and movie domains. Furthermore, we observed that closely related formal domains, such as news and Wikipedia, transfer more effectively. In addition, we introduce a new Yor`ub'a-specific language model (OyoBERT) that outperforms multilingual models in in-domain evaluation. We publicly release the YoNER dataset and pretrained OyoBERT models to support future research on Yor`ub'a natural language processing.