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Abstract
This study investigates lexical development in second language (L2) learning using a complex network method within the framework of complex dynamic system theory (CDST). Analyzing written output from L2 Chinese learners of varying proficiency and language backgrounds, the study differentiates proficiency levels using a bi-gram lexical network model. Lexical network indices (network density, clusters) provide a deeper understanding of L2 proficiency than traditional indices (average word length, hapax legomena percentage, though Guiraud index proves robust). The study reveals consistent complex network characteristics across proficiency levels and highlights the value of word association features beyond word frequency in characterizing interlanguage systems. The authors argue for integrating both lexical frequency and network features in CDST studies of L2 lexical development.
Publisher
Humanities & Social Sciences Communications
Published On
Oct 25, 2023
Authors
Heng Chen
Tags
lexical development
second language learning
complex network method
bi-gram lexical network
proficiency levels
word association
interlanguage systems
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