Year: 2022
Type: conference-paper
Abstract: Understanding child language development requires accurately representing children's lexicons.However, much of the past work modeling children's vocabulary development has utilized adult-based measures.The present investigation asks whether using corpora that captures the language input of young children more accurately represents children's vocabulary knowledge.We present a newlycreated toddler corpus that incorporates transcripts of child-directed conversations, the text of picture books written for preschoolers, and dialog from G-rated movies to approximate the language input a North American preschooler might hear.We evaluate the utility of the new corpus for modeling children's vocabulary development by building and analyzing different semantic network models and comparing them to norms based on vocabulary norms for toddlers in this age range.More specifically, the relations between words in our semantic networks were derived from skip-gram neural networks (Word2Vec) trained on our toddler corpus or on Google news.Results revealed that the models built from the toddler corpus were more accurate at predicting toddler vocabulary growth than the adultbased corpus.These results speak to the importance of selecting a corpus that matches the population of interest.
Authors: Jennifer Weber, Eliana Colunga
Institution: University of Colorado Boulder
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7160268236