The Structure of Lexical-Semantic Networks at Global and Local Levels: A Comparison between L1 and L2

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Complexity Pub Date : 2024-05-14 DOI:10.1155/2024/8644384
Xuefang Feng, Jie Liu
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Abstract

This article applies quantitative methods from complex network analysis to investigate and compare the organization of L1 and L2 lexical-semantic networks. Forty-eight English learners with Chinese as their native language completed a semantic fluency task, first in English and then in Chinese, based on which two lexical-semantic networks were constructed. Comparison at the global level found that the L1 lexical-semantic network displays more prominent small-world and scale-free features and a clearer modular structure in comparison with its L2 counterpart. Locally, although the two lexical-semantic networks share most of their central words, they differ remarkably in their composition and the connection pattern of their peripheral words. Specifically, L1 peripheral words are likely to connect with each other to form local modules while L2 peripheral words tend to connect with central words. Moreover, word centrality was found to be closely related to time of generation, generation frequency, and accuracy in fluency tasks, and such tendency is more obvious in L1 than in L2. The findings demonstrate the advantages of quantitative analysis granted by network science in the investigation of mental lexicon and provide insights for lexical representation research and classroom vocabulary instructions.

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词汇-语义网络在整体和局部层面的结构:L1 和 L2 的比较
本文运用复杂网络分析的定量方法来研究和比较母语和第二语言词汇-语义网络的组织情况。48名以汉语为母语的英语学习者分别用英语和汉语完成了一项语义流畅性任务,并在此基础上构建了两个词义网络。在全局层面的比较发现,与母语为中文的学习者相比,母语为英语的学习者的词汇-语义网络显示出更突出的小世界和无标度特征,以及更清晰的模块化结构。从局部来看,虽然两个词义网络的大部分中心词是相同的,但它们在外围词的构成和连接模式上却有显著差异。具体来说,L1 的外围词很可能相互连接形成局部模块,而 L2 的外围词则倾向于与中心词连接。此外,研究还发现词的中心性与生成时间、生成频率和流利性任务的准确性密切相关,而且这种倾向在 L1 中比在 L2 中更为明显。这些研究结果证明了网络科学在心理词汇研究中的定量分析优势,并为词汇表征研究和课堂词汇教学提供了启示。
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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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