Distribution of sentence length and dependency distance in children's compositions: Characteristics of natural language and variations in language development

• Published in: F1000 research 2023, Vol.12, p.379-379
• Main Author: Imada, Mizuho
• Format: Article
• Language: eng ; jpn
• Subjects: Child; children's compositions; dependency distance; Depressive Disorder, Major; eng; generalized linear mixed model; Humans; Language; Language Development; probability distribution; sentence length; 文長
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.132383.1

This study analyzed the distribution of the sentence length and mean of dependency distances (MDD) in Japanese sentences, comparing data from random sources with that obtained from children's compositions, and identifying changes in distribution according to grade level. Findings reveal that the sentence length in random data is well suited to a geometric distribution, whereas MDD is well suited to a lognormal distribution. In contrast, data from children's compositions show a shift in the distribution of the number of clauses from a lognormal to a gamma distribution, depending on the school year, with MDD suiting a gamma distribution. Mean MDD increases exponentially with the logarithm of the number of clauses in random data, while it increases linearly in composition data, thus generally supporting previous findings that dependency distances are optimized in natural language. However, MDDs exhibit non-monotonic changes with grades, suggesting the complexity of children's language development.