Natural Language Processing (NLP) has seen significant advancements in recent years, but it remains underutilized for African languages due to linguistic and cultural diversity. The research employs a comparative analysis approach, examining existing NLP frameworks for African languages from academic literature and industry applications. A key finding is that lack of standardised corpora and specialized linguistic knowledge significantly hinders the development of NLP models tailored for African languages in Ghana. This deficit affects both model accuracy and user engagement. Despite these challenges, there are opportunities to leverage existing resources and develop innovative solutions, such as hybrid models combining traditional methods with machine learning techniques. Investment should be prioritised in the development of large-scale annotated datasets for African languages. Collaborative efforts between academia and industry can accelerate progress. Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Boateng et al. (Wed,) studied this question.
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