Natural Language Processing (NLP) is a critical component of modern technology, enabling machines to understand human language for tasks such as translation and sentiment analysis. The methodology involves conducting surveys among language experts and developers, analysing existing NLP models, and designing new algorithms tailored for African languages, specifically focusing on the Bantu family of languages used in Gabon. A preliminary survey revealed that over 60% of local developers face challenges related to data scarcity when training NLP models for their native language. This finding emphasizes the need for more localized datasets and improved algorithmic approaches. Despite these challenges, there is significant potential for enhancing NLP capabilities in African languages through targeted research and development initiatives. Developers should prioritise collecting and sharing linguistic data to improve model accuracy, while researchers could focus on developing hybrid models that leverage both local and global language resources. Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Emmanuel Mbongué (Fri,) studied this question.