A Lexical Network Approach to Second Language Development

Abstract

Abstract This study investigates lexical development in second language (L2) learning from the perspective of complex dynamic system theory (CDST) using a complex network method. Based on authentic written output texts from L2 Chinese learners of different proficiency levels and language backgrounds, we successfully differentiate between different proficiency levels using a bi-gram lexical network model. Furthermore, we compare the lexical-network-based approach with the traditional lexical-complexity-based approach. The results show that, compared to traditional lexical complexity indices (such as Average Word Length and Hapax Legomena Percentage), the lexical network indices (such as network size, number of edges, network density, and network centrality) offer greater insight into distinguishing L2 proficiencies and approximating the target language. Furthermore, the findings reveal that L2 Chinese lexical networks exhibit the characteristics of complex networks consistently across all proficiency levels. Additionally, lexical aggregation features, encompassing more than just word frequency information, provide comprehensive properties of the interlanguage system, as supported by their information gain values. We argue that studies within the CDST framework should integrate both lexical complexity and lexical network features to gain a comprehensive understanding of L2 lexical development.