In this study, results of computational simulations on English child-directed speech are presented to uncover what distribu- tional properties of words make it easier to group them into lexical categories. This analysis provides evidence that words are easier to categorize when (i) they are hard to predict given the 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 developmental evidence 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 category acquisition and confirm that it is a robust, reliable, and devel- opmentally plausible source to learn lexical categories.
|Title of host publication||Proceedings of the 39th Annual Conference of the Cognitive Science Society|
|Publisher||Cognitive Science Society|
|Number of pages||6|
|Publication status||Published - Jul 2017|
Cassani, G., Grimm, R., Gillis, S., & Daelemans, W. (2017). Distributional learning and lexical category acquisition: what makes words easy to categorize? In Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 216-221). Cognitive Science Society.