Comparison of ancient and modern Chinese based on complex weighted networks

Abstract

In this study, we compare statistical properties of ancient and modern Chinese within the framework of weighted complex networks. We examine two language networks based on different Chinese versions of the Records of the Grand Historian. The comparative results show that Zipf’s law holds and that both networks are scale-free and disassortative. The interactivity and connectivity of the two networks lead us to expect that the modern Chinese text would have more phrases than the ancient Chinese one. Furthermore, by considering some of the topological and weighted quantities, we find that expressions in ancient Chinese are briefer than in modern Chinese. These observations indicate that the two languages might have different linguistic mechanisms and combinatorial natures, which we attribute to the stylistic differences and evolution of written Chinese.

Comparing the Performance Characteristics and Commonalities of Chinese Modern and Contemporary Literature in Different Cultural Contexts by Combining Text Mining Techniques

“…By analyzing the historical background of the development of modern Chinese literature and literary works in the first half of the 20th century, the authors discuss the changes and innovations of modern Chinese literature under the influence of ideology. The authors of the literature [16] conducted a comparative study of the linguistic structure, lexical use, and grammatical evolution of ancient and modern Chinese by using the analysis method of complex weighted networks. The study results show certain differences between ancient and modern Chinese in terms of language structure and lexical use, but there are certain similarities in grammatical evolution.…”

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.…”