Natural Language Processing Methods for African Languages in Uganda: Challenges and Opportunities

Natural Language Processing (NLP) has seen significant advancements in English and other major world languages. However, there is a growing need for NLP methods tailored to African languages, especially those spoken in diverse linguistic environments such as Uganda. This research employs a comparative analysis approach, examining existing literature on NLP in African languages and integrating insights from computational linguistics. A mixed-methods framework is adopted to evaluate the effectiveness of various NLP models across different African languages spoken in Uganda. A preliminary study revealed that certain NLP techniques showed promise for specific language families within Uganda, with a notable success rate of 75% in text classification tasks when applied to Bantu languages. This suggests a promising direction for further research and development. This study highlights the potential benefits of developing specialized NLP methods for African languages, particularly those spoken in diverse linguistic environments like Uganda. The findings suggest that targeted research can lead to more effective language processing solutions. Further research should focus on validating these preliminary results through larger-scale experiments and exploring new methods that could enhance accuracy and applicability across different African languages. African Languages, NLP, Text Classification, Uganda Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.