Natural Language Processing Challenges and Opportunities in African Languages of Togo

Natural Language Processing (NLP) is a critical component of modern computational systems that process human language. Despite its widespread use in widely spoken languages, NLP techniques for African languages remain underexplored and often face significant challenges. A comparative approach was adopted to evaluate different NLP methodologies. A Maximum Likelihood Estimation (MLE) model was selected as the primary methodological tool due to its robustness in handling sparse data typical of minority languages. The effectiveness of this choice was assessed using a confidence interval around the estimated parameters. The empirical results indicated that the MLE model significantly improved the accuracy of language classification tasks, achieving an accuracy rate of over 90% on a test dataset with a 2-sigma uncertainty level. This study provides valuable insights into the development of NLP models for African languages and highlights the potential benefits of using robust statistical methods in under-resourced language domains. Future research should focus on expanding the MLE model to include additional linguistic features that may enhance its performance, particularly when dealing with more complex Togolese dialects. 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.