Lexical-semantic network growth in children from different socio-economic backgrounds
A mother’s educational background and socio-economic status (SES) have been shown to have significant influence on the child’s early vocabulary development. For example, children coming from families of higher SES generally have larger vocabulary size than children from families of lower SES at the same age during early development. Although vocabulary size is a key indicator of early language development, vocabularies of children from different backgrounds may also differ in other dimensions, such as vocabulary diversity and structural complexity. However, most current studies on this topic focus on vocabulary size and we have very limited knowledge about how the socio-economic background affects vocabulary development in other dimensions. To fill the research gap, the main goal of this project is to characterize the vocabulary structure in children of different SES by analyzing the lexical-semantic network (a structure representing relations between words). Additionally, the lexical-semantic network of maternal child-directed speech (CDS) was also examined to look for correlations between the mother’s and the child’s vocabulary.
The data used in this project contains more than 5.5 million words from 13 corpora in the Child Language Data Exchange System (CHILDES). An efficient pipeline was developed to clean and analyze large language datasets using Python. For quality control, mean length of utterance by morphemes (MLU-m), a common way to measure a child’s linguistic productivity, was used as an indicator of data quality.
This project demonstrates the possibility of studying early vocabulary development with semantic network analysis using a combined source of data. In addition to child language development, the data analysis framework developed in this project can also be used in other studies such as adult language acquisition and bilingualism.
Timeline: This work is close to completion and is expected to be published in 2023.