Contextualising Levels of Language Resourcedness affecting Digital
  Processing of Text
C. Maria Keet Langa Khumalo
Application domains such as digital humanities and tool like chatbots involve
some form of processing natural language, from digitising hardcopies to speech
generation. The language of the content is typically characterised as either a
low resource language (LRL) or high resource language (HRL), also known as
resource-scarce and well-resourced languages, respectively. African languages
have been characterized as resource-scarce languages (Bosch et al. 2007;
Pretorius & Bosch 2003; Keet & Khumalo 2014) and English is by far the most
well-resourced language. Varied language resources are used to develop software
systems for these languages to accomplish a wide range of tasks. In this paper
we argue that the dichotomous typology LRL and HRL for all languages is
problematic. Through a clear understanding of language resources situated in a
society, a matrix is developed that characterizes languages as Very LRL, LRL,
RL, HRL and Very HRL. The characterization is based on the typology of
contextual features for each category, rather than counting tools, and
motivation is provided for each feature and each characterization. The
contextualisation of resourcedness, with a focus on African languages in this
paper, and an increased understanding of where on the scale the language used
in a project is, may assist in, among others, better planning of research and
implementation projects. We thus argue in this paper that the characterization
of language resources within a given scale in a project is an indispensable
component particularly in the context of low-resourced languages.