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- sl:arxiv_author :
- sl:arxiv_firstAuthor : David Ifeoluwa Adelani
- sl:arxiv_num : 2103.11811
- sl:arxiv_published : 2021-03-22T13:12:44Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : MasakhaNER: Named Entity Recognition for African Languages@en
- sl:arxiv_updated : 2021-07-05T15:14:32Z
- sl:bookmarkOf : https://arxiv.org/abs/2103.11811
- sl:creationDate : 2021-07-06
- sl:creationTime : 2021-07-06T13:08:36Z
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