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MasakhaNER: Named Entity Recognition for African Languages

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>22/03/2021
Publication StatusPublished
<mark>Original language</mark>English


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.

Bibliographic note

Accepted at the AfricaNLP Workshop @EACL 2021