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Integrating the Syntactic and Semantic Acquisition of Chinese Classifiers in Mandarin-Speaking Children: A Corpus Study

Title: Integrating the Syntactic and Semantic Acquisition of Chinese Classifiers in Mandarin-Speaking Children: A Corpus Study
Language: English
Authors: Aijun Huang (ORCID 0000-0002-0744-8807); Yunqi He (ORCID 0009-0001-8060-3551)
Source: First Language. 2026 46(2):256-287.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 32
Publication Date: 2026
Document Type: Journal Articles; Reports - Research
Descriptors: Syntax; Semantics; Language Acquisition; Mandarin Chinese; Form Classes (Languages); Children; Child Language; Nouns
DOI: 10.1177/01427237251371761
ISSN: 0142-7237; 1740-2344
Abstract: This study investigates the acquisition of Chinese classifiers in Mandarin-speaking children through an in-depth quantitative analysis of over 200,000 natural utterances from the Child Language Data Exchange System database. The dataset includes interactions from 889 children aged 1 year and 2 months to 6 years, as well as their caregivers. By integrating syntactic and semantic analyses, the research reveals a dissociation between syntactic and semantic development, supporting a modular learning architecture. Syntactic acquisition develops rapidly, achieving adult-like proficiency by age 2;06. In contrast, semantic acquisition follows a protracted, U-shaped trajectory, characterized by a dynamic shift of learning strategies: initially relying on item-based rote memorization, transitioning to rule generalization accompanied by overextension errors, and undergoing gradual refinement that extends beyond the age of 6. These stages reveal a non-parallel developmental trajectory for syntactic and semantic acquisition, highlighting the complexity of mastering a rich nominal classification system like Mandarin Chinese. By integrating quantitative analysis with large-scale corpus data, we deepen our understanding of the linguistic processes underlying classifier acquisition and contribute to broader theories of language development.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1499979
Database: ERIC