Unsupervised Discovery of Phonological Categories through Supervised Learning of Morphological Rules

Abstract

We describe a case study in the application of {\em symbolic machine learning} techniques for the discovery of linguistic rules and categories. A supervised rule induction algorithm is used to learn to predict the correct diminutive suffix given the phonological representation of Dutch nouns. The system produces rules which are comparable to rules proposed by linguists. Furthermore, in the process of learning this morphological task, the phonemes used are grouped into phonologically relevant categories. We discuss the relevance of our method for linguistics and language technology.