Chinese Writing of Deaf or Hard-of-Hearing Students and Normal-Hearing Peers from Complex Network Approach

Abstract

Deaf or hard-of-hearing individuals usually face a greater challenge to learn to write than their normal-hearing counterparts. Due to the limitations of traditional research methods focusing on microscopic linguistic features, a holistic characterization of the writing linguistic features of these language users is lacking. This study attempts to fill this gap by adopting the methodology of linguistic complex networks. Two syntactic dependency networks are built in order to compare the macroscopic linguistic features of deaf or hard-of-hearing students and those of their normal-hearing peers. One is transformed from a treebank of writing produced by Chinese deaf or hard-of-hearing students, and the other from a treebank of writing produced by their Chinese normal-hearing counterparts. Two major findings are obtained through comparison of the statistical features of the two networks. On the one hand, both linguistic networks display small-world and scale-free network structures, but the network of the normal-hearing students' exhibits a more power-law-like degree distribution. Relevant network measures show significant differences between the two linguistic networks. On the other hand, deaf or hard-of-hearing students tend to have a lower language proficiency level in both syntactic and lexical aspects. The rigid use of function words and a lower vocabulary richness of the deaf or hard-of-hearing students may partially account for the observed differences.

Does Scale-Free Syntactic Network Emerge in Second Language Learning?

“…During the senior high school years, to has the highest degrees, becoming the most important vertex in networks. These findings are consistent with previous findings that function words such as articles and prepositions tend to be hubs of a network (Ferrer-i-Cancho and Solé, 2001; Cong and Liu, 2014; Jin and Liu, 2016). These hubs, i.e., the copula be and other function words, exists even in network P 4 , which may account for the absence of the phase shift, that is, the sudden emergence of scale-free property in L2 learning.…”

Differentiating Interpreting Types: Connecting Complex Networks to Cognitive Complexity

“…The centrality of a node reflects its importance and authority in the complex network. It is usually defined by different measures, such as degree centrality, betweenness centrality and closeness centrality (Jin and Liu, 2016). Degree centrality quantifies the number of edges which are directly linked to the vertices.…”

“…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.…”

Predicting Evaluations of Essay by Computational Graph-Based Features

“…The implicit meaning of the deep structure may be extracted by analysis of the surface structure (Bransford and Johnson, 1972;Fodor, 1974). Jin and Liu (2016) used modern Chinese as an example to study the structural features of human language as a multi-level system using a complex network method. Jin and Liu found that the four network models of Chinese (which are character co-occurrence, word co-occurrence, syntactic relationship, and semantic relationship) present their respective statistical characteristics and reflect the commonalities and connections on all levels of the system.…”