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Application domains such as digital humanities and tool like chatbots involvesome form of processing natural language, from digitising hardcopies to speechgeneration. The language of the content is typically characterised as either alow resource language (LRL) or high resource language (HRL), also known asresource-scarce and well-resourced languages, respectively. African languageshave been characterized as resource-scarce languages (Bosch et al. 2007;Pretorius & Bosch 2003; Keet & Khumalo 2014) and English is by far the mostwell-resourced language. Varied language resources are used to develop softwaresystems for these languages to accomplish a wide range of tasks. In this paperwe argue that the dichotomous typology LRL and HRL for all languages isproblematic. Through a clear understanding of language resources situated in asociety, a matrix is developed that characterizes languages as Very LRL, LRL,RL, HRL and Very HRL. The characterization is based on the typology ofcontextual features for each category, rather than counting tools, andmotivation is provided for each feature and each characterization. Thecontextualisation of resourcedness, with a focus on African languages in thispaper, and an increased understanding of where on the scale the language usedin a project is, may assist in, among others, better planning of research andimplementation projects. We thus argue in this paper that the characterizationof language resources within a given scale in a project is an indispensablecomponent particularly in the context of low-resourced languages.

Contextualising Levels of Language Resourcedness affecting Digital Processing of Text