GiellaLT: an infrastructure for rule-based language technology tool development

Currently, machine learning is presented as the ultimate solution for language technology regardless of use case and application, however, it requires as a starting point a massive amount of curated linguistic data in electronic form that is expected to be high quality and representative of the kind of language usage that the tools will follow. For minority and indigenous languages, this can be an insurmountable task, as digital materials of the necessary sizes do not exist and can not easily be produced. In this article we present an approach we have successfully used for supporting indigenous languages to survive and grow in digital contexts for years, and describe the potential of our approach for African contexts. Our technological solution is a free and open-source infrastructure that enables language experts and users to cooperate on creating linguistic resources like dictionaries and grammatical descriptions. In addition we provide language-independent frameworks to build these into applications that are needed by the language community.