Natural Language Processing (NLP) is a critical area within Computer Science that aims to enable machines to understand and process human language. Despite its widespread applications in English, NLP for African languages has been underexplored, particularly for less commonly used languages like Burundian African languages. The methodology employed an exploratory case study approach, analysing existing datasets from Burundian African languages. A variety of NLP tools were used, including tokenization, stemming, and part-of-speech tagging, tailored to the unique characteristics of these languages. Our analysis revealed that while there is a significant corpus of text available in Burundi's African languages, the heterogeneity across dialects poses substantial challenges for consistent NLP application. We found that approximately 30% of words required special handling due to their distinct phonetic and orthographic features. Despite these challenges, our study demonstrates the feasibility and potential benefits of developing specialized NLP tools for Burundi's African languages, which could lead to more accurate language-specific text analysis systems. Further research should focus on creating comprehensive lexicons and grammatical rules specific to each Burundian African language. Collaborative efforts between linguists and computer scientists are essential to address the unique linguistic complexities. Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Ndayirangé et al. (Thu,) studied this question.