MasakhaNER: Named Entity Recognition for African Languages

Understand

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.