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African NER datasets

African NER datasets are curated text corpora in indigenous and widely spoken African languages annotated with labels that identify and categorize specific real-world entities, such as persons, locations, organizations, and dates. Designed to address the historical scarcity of linguistic resources for low-resource and typologically diverse African language families, these datasets serve as essential benchmarks in natural language processing. They enable researchers and developers to train, evaluate, and fine-tune machine learning models for entity extraction, facilitate cross-lingual transfer learning across related languages, and support the creation of digital language technologies tailored to the linguistic and cultural nuances of the African continent.

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MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba O. Alabi, Shamsuddeen Hassan Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing K. Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia V. Taylor, Fatoumata Ouoba Kabore, Chris Chinenye Emezue, Aremu Anuoluwapo, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire Memdjokam Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Elvis Mboning, Tajuddeen Gwadabe, Tosin P. Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo L. Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Adeyemi, Gilles Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu Ngoli, Dietrich Klakow

Why you should read this

Presents the largest human-annotated named entity recognition benchmark across 20 African languages, demonstrating that selecting linguistically related African source languages over English yields an average gain of 14 F1 points in zero-shot cross-lingual transfer.

African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages.

Added

2026-10-03