Feature Selection with Exception Handling - An Example from Phonology

Abstract

The goal in this paper is to show how the classification of phonetic features to phonemes can be acquired. This classificational process is modeled by a supervised feature selection method, based on adaptive distance measures. Exception handling is incorporated into a learned distance function by pointwise additions of Boolean functions for individual pattern combinations. An important result is the differentiation of rules and exceptions during learning.