ABSTRACT Previous studies demonstrate that morphosyntactic plural markers and the structure of numeral systems have individually strong predictive power with regard to the usage of sortal classifiers in languages. We use these two factors as explanatory variables to train the computational classifier of random forests and evaluate the accuracy of their predictive power when selecting the existence/absence of sortal classifiers as response variable. Our results show that these two factors result in an excellent discrimination performance of random forests, even when taking into account sortal classifiers as an areal feature. However, the correlation between morphosyntactic plural markers and multiplicative bases is weaker than the correlation between sortal classifiers and plural markers plus multiplicative bases. We are thus able to provide novel insights with regard to probabilistic universals on sortal classifiers, and suggest an innovative cross-disciplinary approach to test the effect of implicational universals with computational methods.

中文翻译：

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通过计算分类器对世界语言中的分类器分布的统计解释

摘要先前的研究表明，就语言中排序分类器的使用而言，形态句法复数标记和数字系统的结构具有强大的预测能力。我们使用这两个因素作为解释变量来训练随机森林的计算分类器，并在选择排序分类器的存在/不存在作为响应变量时评估其预测能力的准确性。我们的结果表明，即使考虑将分类器作为区域特征，这两个因素也会导致随机森林的出色判别性能。但是，语态句法复数标记和乘法基数之间的相关性比分类分类器和复数标记加乘法基数之间的相关性弱。