African Language Technology: The Data-Driven Perspective

In this paper we outline our recent research efforts, which introduce data-driven methods in the development of language technology components and applications for African languages. Rather than hard-coding the solution to a particular linguistic problem in a set of hand-crafted rules, data-driven methods try to extract the required linguistic classification properties from annotated corpora of the language in question. We describe our efforts to collect and annotate corpora for African languages and show how one can maximise the usability of the (often limited) data with which we are presented. The case studies presented in this paper illustrate the typical advantages of using data-driven methods in the context of natural language processing, namely language independence, development speed, robustness and empiricism.