Effects of Annotations’ Density on Named Entity Recognition Models’ Performance in the Context of African Languages

Analysing the effects of transfer learning on low-resourced named entity recognition performance

Transfer learning has led to large gains in performance for nearly all NLP tasks while making downstream models easier and faster to train. This has also been extended to low-resourced languages, with some success. We investigate the properties of transfer learning

Named entity recognition in a South African context

MasakhaNER: Named Entity Recognition for African Languages

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 stake

Named entity recognition for African languages a focus on the Igbo language

Named Entity Recognition (NER) is a crucial task for many downstream NLP applications, including te

On the Strength of Character Language Models for Multilingual Named Entity Recognition

Character-level patterns have been widely used as features in English Named Entity Recognition (NER)

MasakhaNER: Named Entity Recognition Dataset for 20 African languages.