![[2502.17364] Bridging Gaps in Natural Language Processing for Yorùbá: A Systematic Review of a Decade of Progress and Prospects](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2502.17364] Bridging Gaps in Natural Language Processing for Yorùbá: A Systematic Review of a Decade of Progress and Prospects
Summary
This systematic review analyzes a decade of progress in Natural Language Processing (NLP) for the Yorùbá language, highlighting challenges and opportunities for future research.
Why It Matters
The review addresses the significant underrepresentation of African languages in NLP, specifically Yorùbá, which is crucial for enhancing linguistic diversity in AI technologies. By identifying gaps and resources, it paves the way for more inclusive NLP advancements that can benefit speakers of under-resourced languages.
Key Takeaways
- Yorùbá language faces challenges in NLP due to limited resources and datasets.
- Key obstacles include the scarcity of annotated corpora and pre-trained models.
- The review highlights the importance of addressing socio-cultural factors affecting language use in digital contexts.
- Prominent techniques in Yorùbá NLP include rule-based methods.
- The findings provide a foundation for future research and development in NLP for under-resourced languages.
Computer Science > Computation and Language arXiv:2502.17364 (cs) [Submitted on 24 Feb 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Bridging Gaps in Natural Language Processing for Yorùbá: A Systematic Review of a Decade of Progress and Prospects Authors:Toheeb Aduramomi Jimoh, Tabea De Wille, Nikola S. Nikolov View a PDF of the paper titled Bridging Gaps in Natural Language Processing for Yor\`ub\'a: A Systematic Review of a Decade of Progress and Prospects, by Toheeb Aduramomi Jimoh and Tabea De Wille and Nikola S. Nikolov View PDF HTML (experimental) Abstract:Natural Language Processing (NLP) is becoming a dominant subset of artificial intelligence as the need to help machines understand human language looks indispensable. Several NLP applications are ubiquitous, partly due to the myriad of datasets being churned out daily through mediums like social networking sites. However, the growing development has not been evident in most African languages due to the persisting resource limitations, among other issues. Yorùbá language, a tonal and morphologically rich African language, suffers a similar fate, resulting in limited NLP usage. To encourage further research towards improving this situation, this systematic literature review aims to comprehensively analyse studies addressing NLP development for Yorùbá, identifying challenges, resources, techniques, and applications. A well-defined search string from a structured protocol was employed to search, select...