David Ifeoluwa Adelani, Jade Z. 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 Hassan Muhammad, Chris C. Emezue, J. Nakatumba‐Nabende, Perez Ogayo, Anuoluwapo Aremu, Catherine Gitau, Derguene Mbaye, Jesujoba Oluwadara Alabi, Seid Muhie Yimam, T. Gwadabe, I. Ezeani, Andre Niyongabo Rubungo, Jonathan Mukiibi, V. Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin P. Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, E. Anebi, C. Chukwuneke, N. Odu, Eric Peter Wairagala, S. 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, A. Faye, Blessing K. Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, T. Diop, A. Diallo, Adewale Akinfaderin, T. Marengereke, Salomey Osei

TACL

Paper

Abstract

Abstract We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1