Bootstrapping NLP tools across low-resourced African languages: an overview and prospects
This work addresses the need for local content and tools in indigenous African languages, which is crucial for growing computing markets in Southern Africa, but it is largely an incremental overview of existing efforts.
The paper tackles the challenge of developing NLP tools for low-resourced African languages by reviewing bootstrapping efforts, particularly for Niger-Congo B languages, finding that grammar-based methods show positive outcomes across distant languages, while data-driven approaches face difficulties due to lexical diversity.
Computing and Internet access are substantially growing markets in Southern Africa, which brings with it increasing demands for local content and tools in indigenous African languages. Since most of those languages are low-resourced, efforts have gone into the notion of bootstrapping tools for one African language from another. This paper provides an overview of these efforts for Niger-Congo B (`Bantu') languages. Bootstrapping grammars for geographically distant languages has been shown to still have positive outcomes for morphology and rules or grammar-based natural language generation. Bootstrapping with data-driven approaches to NLP tasks is difficult to use meaningfully regardless geographic proximity, which is largely due to lexical diversity due to both orthography and vocabulary. Cladistic approaches in comparative linguistics may inform bootstrapping strategies and similarity measures might serve as proxy for bootstrapping potential as well, with both fertile ground for further research.