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The lack of labeled training data has limited the development of naturallanguage processing tools, such as named entity recognition, for many languagesspoken in developing countries. Techniques such as distant and weak supervisioncan be used to create labeled data in a (semi-) automatic way. Additionally, toalleviate some of the negative effects of the errors in automatic annotation,noise-handling methods can be integrated. Pretrained word embeddings areanother key component of most neural named entity classifiers. With the adventof more complex contextual word embeddings, an interesting trade-off betweenmodel size and performance arises. While these techniques have been shown towork well in high-resource settings, we want to study how they perform inlow-resource scenarios. In this work, we perform named entity recognition forHausa and Yor\`ub\'a, two languages that are widely spoken in severaldeveloping countries. We evaluate different embedding approaches and show thatdistant supervision can be successfully leveraged in a realistic low-resourcescenario where it can more than double a classifier's performance.

Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yor\`ub\'a