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MasakhaNER: Named Entity Recognition for African Languages

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 Muhammad
Chris Chinenye Emezue
Joyce Nakatumba-Nabende
Perez Ogayo
Anuoluwapo Aremu
Catherine Gitau
Derguene Mbaye
Jesujoba Alabi
Seid Muhie Yimam
Tajuddeen 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
TACL (2021)

Abstract

We take a step towards addressing the underrepresentation of the African continent in NLP research by creating the first large publicly available high­quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders. We detail characteristics of the languages to help researchers understand the challenges that these languages pose for NER. We analyze our datasets and conduct an extensive empirical evaluation of state­of­the­art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP.