MasakhaNER: Named Entity Recognition for African Languages
This addresses the problem of limited NLP resources for African languages, benefiting researchers and communities, but it is incremental as it applies existing methods to new data.
The authors tackled the under-representation of African languages in NLP by creating the first large, high-quality NER dataset for ten African languages, and they conducted empirical evaluations of state-of-the-art methods, releasing data and models to inspire future research.
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.