Learning distributed linguistic classes

Abstract

Error-correcting output codes (ECOC) have emerged in machine learning as a successful implementation of the idea of distributed classes. Monadic class symbols are replaced by bit strings, which are learned by an ensemble of binary-valued classifiers (dichotomizers). In this study, the idea of ECOC is applied to memory-based language learning with local (k-nearest neighbor) classifiers. Regression analysis of the experimental results reveals that, in order for ECOC to be successful for language learning, the use of the Modified Value Difference Metric (MVDM) is an important factor, which is explained in terms of population density of the class hyperspace.