---
title: "MasakhaNER: Named Entity Recognition for — Adelani 2021"
description: "African languages are largely missing from NLP resources, and NER in particular has had almost no public, human-annotated benchmarks for them."
authors: ["David Ifeoluwa Adelani", "Jade Abbott", "Graham Neubig", "Daniel D'souza", "Julia Kreutzer", "Constantine Lignos", "Chester Palen-Michel", "Happy Buzaaba", "Shruti Rijhwani", "Sebastian Ruder", "Stephen Mayhew", "Israel Abebe Azime", "Shamsuddeen H. Muhammad", "Chris Chinenye Emezue", "Joyce Nakatumba-Nabende", "Perez Ogayo", "Aremu Anuoluwapo", "Catherine Gitau", "Derguene Mbaye", "Jesujoba Alabi", "Seid Muhie Yimam", "Tajuddeen Rabiu Gwadabe", "Ignatius Ezeani", "Rubungo Andre Niyongabo", "Jonathan Mukiibi", "Verrah Otiende", "Iroro Orife", "Davis David", "Samba Ngom", "Tosin Adewumi", "Paul Rayson", "Mofetoluwa Adeyemi", "Gerald Muriuki", "Emmanuel Anebi", "Chiamaka Chukwuneke", "Nkiruka Odu", "Eric Peter Wairagala", "Samuel Oyerinde", "Clemencia Siro", "Tobius Saul Bateesa", "Temilola Oloyede", "Yvonne Wambui", "Victor Akinode", "Deborah Nabagereka", "Maurice Katusiime", "Ayodele Awokoya", "Mouhamadane MBOUP", "Dibora Gebreyohannes", "Henok Tilaye", "Kelechi Nwaike", "Degaga Wolde", "Abdoulaye Faye", "Blessing Sibanda", "Orevaoghene Ahia", "Bonaventure F. P. Dossou", "Kelechi Ogueji", "Thierno Ibrahima DIOP", "Abdoulaye Diallo", "Adewale Akinfaderin", "Tendai Marengereke", "Salomey Osei"]
year: 2021
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2021.tacl-1.66/"
doi: "10.1162/tacl_a_00416"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/"
---
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# MasakhaNER: Named Entity Recognition for African Languages

**Authors:** [David Ifeoluwa Adelani](https://md.research.elamur.ai/free/en/authors/david-ifeoluwa-adelani/), Jade Abbott, [Graham Neubig](https://md.research.elamur.ai/free/en/authors/graham-neubig/), [Daniel D'souza](https://md.research.elamur.ai/free/en/authors/daniel-d-souza/), [Julia Kreutzer](https://md.research.elamur.ai/free/en/authors/julia-kreutzer/), [Constantine Lignos](https://md.research.elamur.ai/free/en/authors/constantine-lignos/), [Chester Palen-Michel](https://md.research.elamur.ai/free/en/authors/chester-palen-michel/), Happy Buzaaba, [Shruti Rijhwani](https://md.research.elamur.ai/free/en/authors/shruti-rijhwani/), [Sebastian Ruder](https://md.research.elamur.ai/free/en/authors/sebastian-ruder/), Stephen Mayhew, [Israel Abebe Azime](https://md.research.elamur.ai/free/en/authors/israel-abebe-azime/) et al. (61 authors) · **Year:** 2021 · **Venue:** Transactions of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.tacl-1.66/) · [PDF](https://aclanthology.org/2021.tacl-1.66.pdf) · [DOI 10.1162/tacl\_a\_00416](https://doi.org/10.1162/tacl_a_00416) · [OpenAlex](https://openalex.org/W3207937903)

**Citations:** 257 citations (openalex-snapshot · 2026-10-14) · [172](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
African languages are largely missing from NLP resources, and NER in particular has had almost no public, human-annotated benchmarks for them. A large community effort coordinated through Masakhane, which brought together native speakers, annotators, and NLP researchers, built MasakhaNER. It is the first sizable, high-quality, openly released NER dataset for ten widely spoken African languages: Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Luo, Nigerian-Pidgin, Swahili, Wolof, and Yorùbá. The texts come from local news sources, so the entities reflect what matters to speakers of each language. The authors describe the typology and orthography of each language, including scripts, tone, and affixation. They then benchmark a CNN-BiLSTM-CRF baseline and fine-tuned multilingual transformers (mBERT, XLM-R) in supervised, cross-domain, cross-lingual, and regional multi-language settings. Key findings:
- Transfer from geographically and linguistically close African languages beats transfer from English.
- Pooling training data across languages gives modest gains.
- Unseen and multi-word entities remain hard.

The data, code, and models are public, which gives African NLP a reproducible starting point.

## Key points
- Entity types annotated are PER, LOC, ORG, and DATE. The languages span Afro-Asiatic, Niger-Congo, Nilo-Saharan, and an English Creole, with speaker counts ranging from about 4M (Luo) to 98M (Swahili).
- Zero-shot cross-lingual transfer with XLM-R-base:
  - A model trained on only about 2K Nigerian-Pidgin sentences performs about as well as one trained on eng-CoNLL03 (about 14K sentences).
  - Hausa as the source language gives the best average F1, more than 16 points above eng-CoNLL03.
- Cross-domain transfer from WikiAnn (Wikipedia) to news is weak. Zero-shot F1 stays below 40 for all five languages tested, and below 10 for Kinyarwanda and Yorùbá.
- Training one model on all nine non-Amharic languages helps every language except Swahili. Across ibo, kin, lug, luo, wol, and yor, the average gain is 1.6 F1.
- Error analysis shows two main weak spots:
  - Entities never seen in training lose about 5 F1 points.
  - Entities of four or more words lose about 20 F1 points.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/)

## Cited by

Cited by

172 papers of our library cite it (of the 140884 whose bibliographies we read); 17 of them have a page here, 155 not yet.

- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://md.research.elamur.ai/free/en/papers/adelani-2022-few-thousand-translations-go-long/)
- [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/adelani-2022-masakhaner-2-0-africa-centric/)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://md.research.elamur.ai/free/en/papers/alabi-2022-adapting-pre-trained-language-models/)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/)
- [Challenges and Strategies in Cross-Cultural NLP](https://md.research.elamur.ai/free/en/papers/hershcovich-2022-challenges-strategies-cross-cultural-nlp/)
- [ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer](https://md.research.elamur.ai/free/en/papers/jibril-2022-anec-amharic-named-entity-corpus/)
- [Expanding the Lexicon of Ge'ez Based African Languages: A Comparative Study of Amharic and Tigrinya](https://md.research.elamur.ai/free/en/papers/langstream-2026-teklehaymanot-expanding-lexicon-ge-ez-based/)
- [The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset](https://md.research.elamur.ai/free/en/papers/lauren-2023-ccon-bigscience-roots-corpus-1-6tb/)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/muhammad-2022-naijasenti-nigerian-twitter-sentiment-corpus/)
- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://md.research.elamur.ai/free/en/papers/muhammad-2023-afrisenti-twitter-sentiment-analysis-benchmark/)
- [Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens](https://md.research.elamur.ai/free/en/papers/nigatu-2026-evaluating-machine-translation-datasets-low/)
- [Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages](https://md.research.elamur.ai/free/en/papers/ogueji-2021-small-data-no-problem-exploring/)
- [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2022-lifting-curse-multilinguality-pre-training/)
- [Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/)
- [LGSE: Lexically Grounded Subword Embedding Initialization for Low-Resource Language Adaptation](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2026-lgse-lexically-grounded-subword-embedding/)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
- [Ethical and social risks of harm from Language Models](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{adelani2021masakhaner,
  title = {{MasakhaNER: Named Entity Recognition for African Languages}},
  author = {Adelani, David Ifeoluwa and Abbott, Jade and Neubig, Graham and D'souza, Daniel and Kreutzer, Julia and Lignos, Constantine and Palen-Michel, Chester and Buzaaba, Happy and Rijhwani, Shruti and Ruder, Sebastian and Mayhew, Stephen and Azime, Israel Abebe and Muhammad, Shamsuddeen H. and Emezue, Chris Chinenye and Nakatumba-Nabende, Joyce and Ogayo, Perez and Anuoluwapo, Aremu and Gitau, Catherine and Mbaye, Derguene and Alabi, Jesujoba and Yimam, Seid Muhie and Gwadabe, Tajuddeen Rabiu and Ezeani, Ignatius and Niyongabo, Rubungo Andre and Mukiibi, Jonathan and Otiende, Verrah and Orife, Iroro and David, Davis and Ngom, Samba and Adewumi, Tosin and Rayson, Paul and Adeyemi, Mofetoluwa and Muriuki, Gerald and Anebi, Emmanuel and Chukwuneke, Chiamaka and Odu, Nkiruka and Wairagala, Eric Peter and Oyerinde, Samuel and Siro, Clemencia and Bateesa, Tobius Saul and Oloyede, Temilola and Wambui, Yvonne and Akinode, Victor and Nabagereka, Deborah and Katusiime, Maurice and Awokoya, Ayodele and MBOUP, Mouhamadane and Gebreyohannes, Dibora and Tilaye, Henok and Nwaike, Kelechi and Wolde, Degaga and Faye, Abdoulaye and Sibanda, Blessing and Ahia, Orevaoghene and Dossou, Bonaventure F. P. and Ogueji, Kelechi and DIOP, Thierno Ibrahima and Diallo, Abdoulaye and Akinfaderin, Adewale and Marengereke, Tendai and Osei, Salomey},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2021},
  doi = {10.1162/tacl\_a\_00416},
  url = {https://doi.org/10.1162/tacl_a_00416}
}
```

- [← previous Manually Annotated Spelling Error Corpus for Amharic](https://md.research.elamur.ai/free/en/papers/gezmu-2021-manually-annotated-spelling-error-corpus/)
- [next → Masked Autoencoders Are Scalable Vision Learners](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/3/)

## References

2 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. D. Adelani, Dana Ruiter, J. Alabi, Damilola Adebonojo, Adesina Ayeni, Mofetoluwa Adeyemi, Ayodele Awokoya, and C. España-Bonet. 2021. MENYO-20k: A multi-domain english-yorùbá corpus for machine translation and domain adaptation. ArXiv, abs/2103.08647. — [arXiv:2103.08647](https://arxiv.org/abs/2103.08647) [find](https://scholar.google.com/scholar?q=D%2E+Adelani%2C+Dana+Ruiter%2C+J%2E+Alabi%2C+Damilola+Adebonojo%2C+Adesina+Ayeni%2C+Mofetoluwa+Adeyemi%2C+Ayodele+Awokoya%2C+and+C%2E+Espan%CC%83a%2DBonet%2E+2021%2E+MENYO%2D20k%3A+A+multi%2Ddomain+english%2Dyoru%CC%80ba%CC%81+corpus+for+machine+tra)
2. Željko Agić and Ivan Vulić. 2019. JW300: A wide-coverage parallel corpus for low-resource languages. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3204–3210, Florence, Italy. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Z%CC%8Celjko+Agic%CC%81+and+Ivan+Vulic%CC%81%2E+2019%2E+JW300%3A+A+wide%2Dcoverage+parallel+corpus+for+low%2Dresource+languages%2E+In+Proceedings+of+the+57th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+page) [our card](https://md.research.elamur.ai/free/en/papers/agic-2019-jw300-wide-coverage-parallel-corpus/)