# NaijaNER : Comprehensive Named Entity Recognition for 5 Nigerian Languages

arXiv: https://arxiv.org/abs/2105.00810 · HTML: https://mlsanity.com/paper/2105.00810/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Named Entity Recognition in the Style of Object Detection](https://mlsanity.com/paper/2101.11122/) (2021)

## Built on by (most cited first, 10 of 10)

- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [On Language Models for Creoles](https://mlsanity.com/paper/2109.06074/) (2021)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [Detection and Analysis of Offensive Online Content in Hausa Language](https://mlsanity.com/paper/2311.10541/) (2023)
- [ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition](https://mlsanity.com/paper/2502.11198/) (2025)
- [Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin](https://mlsanity.com/paper/2404.18264/) (2024)
- [Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction](https://mlsanity.com/paper/2402.14521/) (2024)
- [NaijaNLP: A Survey of Nigerian Low-Resource Languages](https://mlsanity.com/paper/2502.19784/) (2025)
- [YoNER: A New Yor`ub'a Multi-domain Named Entity Recognition Dataset](https://mlsanity.com/paper/2604.05624/) (2026)
- [Cross-Dialect NER for Bangla Regional Dialects Using Leave-One-Dialect-Out Cross-Validation and Explainable AI](https://mlsanity.com/paper/2609.22536/) (2026)

## Abstract (arXiv)

Most of the common applications of Named Entity Recognition (NER) is on English and other highly available languages. In this work, we present our findings on Named Entity Recognition for 5 Nigerian Languages (Nigerian English, Nigerian Pidgin English, Igbo, Yoruba and Hausa). These languages are considered low-resourced, and very little openly available Natural Language Processing work has been done in most of them. In this work, individual NER models were trained and metrics recorded for each of the languages. We also worked on a combined model that can handle Named Entity Recognition (NER) for any of the five languages. The combined model works well for Named Entity Recognition(NER) on each of the languages and with better performance compared to individual NER models trained specifically on annotated data for the specific language. The aim of this work is to share our learning on how information extraction using Named Entity Recognition can be optimized for the listed Nigerian Languages for inclusion, ease of deployment in production and reusability of models. Models developed during this project are available on GitHub https://git.io/JY0kk and an interactive web app https://nigner.herokuapp.com/.