Parsing IsiZulu Text Using Grammatical Framework

Marais, Laurette; Pretorius, Laurette

doi:10.1007/978-3-031-38318-2_17

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An in-depth description of how the ZRG models isiZulu at a subword level is beyond the scope of this paper: the interested reader is referred to Marais & Pretorius (2023). Relevant to this work is the fact that the ZRG systematically uses a specific token for binding subwords together at runtime to form grammatically correct surface tokens.…”

Section: Datasetsmentioning

confidence: 99%

Generation of segmented isiZulu text

Mkhwanazi,

Marais

2024

JDHASA

View full text Add to dashboard Cite

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.

show abstract

Section: Datasetsmentioning

confidence: 99%