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Inferring case paradigms 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 2677 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 approach. Our work suggests that such a machine learning approach might become part of the complex theoretical and methodological toolkit needed in language description and linguistic theory development.
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Contributor : Nicolas Quint <>
Submitted on : Monday, January 4, 2021 - 5:32:33 PM
Last modification on : Wednesday, January 6, 2021 - 3:28:14 AM


  • HAL Id : halshs-03095796, version 1



Nicolas Quint, Marc Allassonnière-Tang. Inferring case paradigms with computational classifiers. Natural Language Engineering, Cambridge University Press (CUP), In press. ⟨halshs-03095796⟩



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