Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels

Abstract

Over time, interlanguage studies have shifted from early qualitative to quantitative studies of specific linguistic structures. However, the focus of these studies is usually on one aspect of an interlanguage instead of the whole system. The ideal object of interlanguage research is a second language (L2) learner language system, for only in this way can the entire L2 learning process can be examined. As a self-organizing and self-regulated system, the panorama of interlanguage can be revealed objectively through a complex network approach. In this study, we construct eight interlanguage dependency syntactic networks of varying proficiency levels and modalities, and conduct a quantitative study of respective network parameters. We find that all syntactic networks of Chinese L2 learners (English native speakers) initially present scale-free and small-world properties. Additionally, there is no sudden syntactic emergence in interlanguage with different modalities. This suggests varying regularities in the development of a syntactic network between interlanguage and native language acquisition. Moreover, the first language plays an important role in L2 development. The network parameters (<k>), L, C, ND, and NC can differentiate interlanguage modalities, and five quantitative parameters, <k>, C, ND, γ′, and NC, can indicate L2 proficiency.

A Lexical Network Approach to Second Language Development

“…For example, Jiang et al (2019) applied a syntactic network approach and discovered that English L2 learning is characterized by a gradual approximation to the target language, rather than a sudden emergence as in L1 development. In another study, Hao et al (2021) found varying regularities in the development of a syntactic network between interlanguage and native language acquisition, highlighting the network parameters' ability to differentiate interlanguage modalities and indicate L2 pro ciency.…”

“…The network parameters that we are most interested in are those closely related to language pro ciencies (see Jiang et al, 2019;Hao et al, 2021;Chen, 2021), including clustering coe cient (C), average path length (< l>), and clusters (cl), etc. For comparison, we also investigated some common network parameters, such as network size (n), number of edges (m), network density (ND), and network centrality (NC).…”

Differentiating Interpreting Types: Connecting Complex Networks to Cognitive Complexity

“…Prior studies demonstrate the feasibility of using the complex network approach to characterize and classify human languages, including the exploration of statistical patterns of different languages, stylistic research (Ferrer-i-Cancho et al, 2004;Liu, 2008a), typological properties of languages (Liu and Xu, 2011), hierarchical structures of a language (Chen et al, 2015), the evolution of languages (Cui et al, 2017;Chen et al, 2018), and linguistic features in language learning (Jin and Liu, 2016;Jiang et al, 2019;Hao et al, 2021). It is probable that network properties can also discriminate linguistic features of different interpreting types.…”