Inferring case paradigms in Koalib with computational classifiers

Abstract The object case inflection in Koalib (Niger-Congo) represents complex patterns that involve phoneme position, syllable structure, and tonal pattern. Few attempts have been made with qualitative and quantitative approaches to identify the rules of the object case paradigms in Koalib. In the current study, information on phonemes, tones, and syllables are automatically extracted from a Koalib sample of 2,677 lexemes. The data is then fed to decision-tree-based classifiers to predict the object case paradigms and extract the interactive patterns between the variables. The results improve the predicting accuracy of existing studies and identify the case paradigms predicted by linguistic hypotheses. New case paradigms are also found by the computational classifiers and explained from a linguistic perspective. Our work demonstrates that the combination of linguistic theoretical knowledge with machine learning techniques can become one of the methodological approaches for linguistic analyses.