(untitled) (Journal of the Digital Humanities Association of Southern Africa, 2024)
The complex morphology, conjunctive orthography and widespread occurrence of morphophonological alternation in the Nguni languages have given rise to several efforts towards morphological segmentation of tokens of Nguni languages. For supervised methods, annotated data is required, which currently exists as canonically segmented data in the NCHLT corpus and surface segmented data in the Ukwabelana corpus. In this paper, we present a method and segmentation strategy based on a computational grammar for isiZulu. The grammar, which itself has some limitations in processing speed and robustness to unexpected input, is used to create a new set of segmentations for the tokens of the Ukwabelana corpus. By training various models with the same architecture but on different datasets, we first show that our approach enables us to match the performance of a model trained on pre-existing data. We also show that our approach provides the flexibility to determine a suitable segmentation strategy and to generate data that reflects this strategy.