MasakhaNER: Named Entity Recognition for African Languages

• DOI: 10.1162/tacl_a_00416
• Publication Date: 2021-10-17T23:03:50Z
• Authors: David Ifeoluwa Ad...; 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. Mu...; Chris Chinenye Em...; Joyce Nakatumba-N...; Perez Ogayo; Aremu Anuoluwapo; Catherine Gitau; Derguene Mbaye; Jesujoba Alabi; Seid Muhie Yimam; Tajuddeen Rabiu G...; Ignatius Ezeani; Rubungo Andre Niy...; 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....; Kelechi Ogueji; Thierno Ibrahima ...; Abdoulaye Diallo; Adewale Akinfaderin; Tendai Marengereke; Salomey Osei
• Keywords: Named Entity Recognition; Representation; Code (set theory)

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