Developmentally plausible learning of word categories from distributional statistics


In this paper we evaluate a mechanism for the learning of word categories from distributional information against criteria of psychological plausibility. We elaborate on the ideas developed by Redington et al. (1998) by embedding the mechanism in an existing model of language acquisition (MOSAIC) and gradually expanding the contexts it has access to in a developmentally plausible way. In line with child data, the mechanism shows early development of a noun category, and later development of a verb category. It is furthermore shown that the mechanism can maintain high performance at lower computational overhead by disregarding token frequency information, thus improving the plausibility of the mechanism as something that is used by language-learning children.

Back to Table of Contents