Proceedings of the Annual Meeting of the Cognitive Science Society

In this study, results of computational simulations on Englishchild-directed speech are presented to uncover what distribu-tional properties of words make it easier to group them intolexical categories. This analysis provides evidence that wordsare easier to categorize when (i) they are hard to predict giventhe contexts they occur in; (ii) they occur in few different con-texts; and (iii) their contextual distributions have a low entropy,meaning that they tend to occur more often in one of the con-texts they occur in. This profile fits that of content words, espe-cially nouns and verbs, which is consistent with developmentalevidence showing that children learning English start by form-ing a noun and a verb category. These results further charac-terize the role of distributional information in lexical categoryacquisition and confirm that it is a robust, reliable, and devel-opmentally plausible source to learn lexical categories.