학술논문
MasakhaNER: Named Entity Recognition for African Languages
Document Type
article
Author
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
Source
Transactions of the Association for Computational Linguistics, Vol 9, Pp 1116-1131 (2021)
Subject
Language
English
ISSN
2307-387X
Abstract
AbstractWe 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