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.

中文翻译：

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使用计算分类器在 Koalib 中推断案例范式

摘要 Koalib (Niger-Congo) 中的宾格变化表示复杂的模式，涉及音素位置、音节结构和音调模式。很少有人尝试使用定性和定量方法来识别 Koalib 中对象案例范式的规则。在当前的研究中，有关音素、音调和音节的信息是从 2,677 个词素的 Koalib 样本中自动提取的。然后将数据馈送到基于决策树的分类器以预测对象案例范式并提取变量之间的交互模式。结果提高了现有研究的预测准确性，并确定了语言假设预测的案例范式。计算分类器还发现了新的案例范式，并从语言的角度进行了解释。