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.

本文介绍了第一个大型公开高质量的非洲十种语言的命名实体识别数据集，研究这些语言对命名实体识别带来的挑战，并在监督和迁移学习设置下进行了广泛的实证评估和分析，旨在启发未来的非洲自然语言处理研究。

MasakhaNER: 面向非洲语言的命名实体识别