Compared to well-resourced languages such as English and Dutch, natural language processing (NLP) tools for Afrikaans are still not abundant. In the context of the AfriBooms project, KU Leuven and the North-West University collaborated to develop a first, small treebank, a dependency parser, and an easy to use online linguistic search engine for Afrikaans for use by researchers and students in the humanities and social sciences. The search tool is based on a similar development for Dutch, i.e. GrETEL, a user-friendly search engine which allows users to query a treebank by means of a natural language example instead of a formal search instruction.

In most languages, new words can be created through the process of compounding, which combines two or more words into a new lexical unit. Whereas in languages such as English the components that make up a compound are separated by a space, in languages such as Finnish, German, Afrikaans and Dutch these components are concatenated into one word. Compounding is very productive and leads to practical problems in developing machine translators and spelling checkers, as newly formed compounds cannot be found in existing lexicons. The Automatic Compound Processing (AuCoPro) project deals with the analysis of compounds in two closely-related languages, Afrikaans and Dutch. In this paper, we present the development and evaluation of two datasets, one for each language, that contain compound words with annotated compound boundaries. Such datasets can be used to train classifiers to identify the compound components in novel compounds. We describe the process of annotation and provide an overview of the annotation guidelines as well as global properties of the datasets. The inter-rater agreements between the annotators are considered highly reliable. Furthermore, we show the usability of these datasets by building an initial automatic compound boundary detection system, which assigns compound boundaries with approximately 90% accuracy.

The management of language resources requires several legal aspects to be taken into consideration. In this paper we discuss a number of these aspects which lead towards the formation of a legal framework for a language resources management agency. The legal framework entails examination of; the agency's stakeholders and the relationships that exist amongst them, the privacy and intellectual property rights that exist around the language resources offered by the agency, and the external (e.g. laws, acts, policies) and internal legal instruments (e.g. end user licence agreements) required for the agency's operation.

Human language technologies (HLT) can play a vital role in bridging the digital divide and thus the HLT field has been recognised as a priority area by the South African government. We present our work on conducting a technology audit on the South African HLT landscape across the country’s eleven official languages. The process and the instruments employed in conducting the audit are described and an overview of the various complementary approaches used in the results’ analysis is provided. We find that a number of HLT language resources (LRs) are available in SA but they are of a very basic and exploratory nature. Lessons learnt in conducting a technology audit in a young and multilingual context are also discussed.