Using known words to learn more words: A distributional model of child vocabulary acquisition

Abstract

Why do children learn some words before others? A large body of behavioral research has identified properties of the language environment that facilitate word learning, emphasizing the importance of particularly informative language contexts that build on children’s prior knowledge. However, these findings have not informed research that uses distributional properties of words to predict vocabulary composition. In the current work, we introduce a predictor of word learning that emphasizes the role of prior knowledge. We investigate item-based variability in vocabulary development using lexical properties of distributional statistics derived from a large corpus of child-directed speech. Unlike previous analyses, we predicted word trajectories cross-sectionally across child age, shedding light on trends in vocabulary development that may not have been evident at a single time point. We also show that regardless of a word’s grammatical class, the best distributional predictor of whether a child knows a word is the number of other known words with which that word tends to co-occur.