Using Known Words to Learn More Words: A Distributional Analysis of Child Vocabulary Development

TL;DR

This study analyzes how distributional properties of child-directed speech predict the order and variability of vocabulary acquisition in children, revealing that known co-occurring words strongly influence learning trajectories.

Contribution

It introduces a cross-sectional distributional analysis approach to predict vocabulary development, highlighting the role of known co-occurring words in learning.

Findings

Number of known co-occurring words predicts vocabulary knowledge.

Distributional statistics explain variability in word acquisition.

Cross-sectional analysis reveals trends not seen in single time points.

Abstract

Why do children learn some words before others? Understanding individual variability across children and also variability across words, may be informative of the learning processes that underlie language learning. We investigated 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, shedding light on trends in vocabulary development that may not have been evident at a single time point. We also show that whether one looks at a single age group or across ages as a whole, 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. Keywords: age of acquisition; vocabulary development; lexical diversity; child-directed speech;