Natural Language Processing (NLP) has seen significant advancements in handling languages from Western linguistic traditions but faces challenges when applied to African languages, particularly those with limited resources and complex grammatical structures. A systematic approach was employed to develop NLP tools for African languages in Uganda. This involved the collection and annotation of linguistic resources, followed by the design and testing of several NLP models using Python programming language. The methodology also incorporated a comparative analysis with established NLP frameworks to assess applicability. The findings indicate that there is a significant variation (proportion) in model performance across different African languages due to their unique grammatical structures, necessitating customized computational solutions. This study highlights the necessity for tailored NLP methodologies when addressing linguistic diversity. The development of specific tools and models demonstrates progress towards overcoming language barriers in Uganda's digital landscape. Future research should focus on expanding datasets to include a broader spectrum of African languages, promoting interdisciplinary collaboration between linguists and technologists, and advocating for more comprehensive support from funding bodies. Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Ojama et al. (Thu,) studied this question.