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 D. Mayhew, Israel Abebe Azime, Shamsuddeen Hassan Muhammad, Chris Chinenye Emezue, Joyce Nakatumba Nabende, Perez Ogayo, Anuoluwapo Aremu, Catherine Gitau, Derguène Mbaye, Jesujoba Oluwadara Alabi · Transactions of the Association for Computational Linguistics · 2021
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