Decision trees, entropy, and the contrastive feature hierarchy

Abstract

Dresher (2009) argues that language-particular hierarchies of features are the best way to identify contrastive features in a phonological inventory. While not universal, this ordering of features is also not fully unconstrained. But what limits the space of possible feature orders remains an open question. This paper demonstrates how the concept of entropy establishes a partial ordering of features that both allows for but also constrains language-particular variation. Specifically, a decision tree machine learning algorithm is employed to dynamically impose structure on the hypothesis space of possible feature orders.