Natural language processing for African languages

• URL: http://arxiv.org/abs/2507.00297v1
• Date: Mon, 30 Jun 2025 22:26:36 GMT
• Title: Natural language processing for African languages
• Authors: David Ifeoluwa Adelani,
• Abstract summary: dissertation focuses on languages spoken in Sub-Saharan Africa where all the indigenous languages can be regarded as low-resourced.<n>We show that the quality of semantic representations learned in word embeddings does not only depend on the amount of data but on the quality of pre-training data.<n>We develop large scale human-annotated labelled datasets for 21 African languages in two impactful NLP tasks.
• Score: 7.884789325654572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in word embeddings and language models use large-scale, unlabelled data and self-supervised learning to boost NLP performance. Multilingual models, often trained on web-sourced data like Wikipedia, face challenges: few low-resource languages are included, their data is often noisy, and lack of labeled datasets makes it hard to evaluate performance outside high-resource languages like English. In this dissertation, we focus on languages spoken in Sub-Saharan Africa where all the indigenous languages in this region can be regarded as low-resourced in terms of the availability of labelled data for NLP tasks and unlabelled data found on the web. We analyse the noise in the publicly available corpora, and curate a high-quality corpus, demonstrating that the quality of semantic representations learned in word embeddings does not only depend on the amount of data but on the quality of pre-training data. We demonstrate empirically the limitations of word embeddings, and the opportunities the multilingual pre-trained language model (PLM) offers especially for languages unseen during pre-training and low-resource scenarios. We further study how to adapt and specialize multilingual PLMs to unseen African languages using a small amount of monolingual texts. To address the under-representation of the African languages in NLP research, we developed large scale human-annotated labelled datasets for 21 African languages in two impactful NLP tasks: named entity recognition and machine translation. We conduct an extensive empirical evaluation using state-of-the-art methods across supervised, weakly-supervised, and transfer learning settings.

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