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MasakhaNER: Named Entity Recognition for African Languages

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arxiv 2103.11811 v2 pith:A4F5SCDB submitted 2021-03-22 cs.CL cs.AI

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
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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
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classification cs.CL cs.AI
keywords africanlanguagesentitynamedrecognitionresearchacrossaddressing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality 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 state-of-the-art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP.

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