Language is a complex adaptive system, but how does it change? For investigating this process, four diachronic Chinese word co-occurrence networks have been built based on texts that were written during the last 2,000 years. By comparing the network indicators that are associated with the hierarchical features in language networks, we learn that the hierarchy of Chinese lexical networks has indeed evolved over time at three different levels. The connections of words at the micro level are continually weakening; the number of words in the meso-level communities has increased significantly; and the network is expanding at the macro level. This means that more and more words tend to be connected to medium-central words and form different communities. Meanwhile, fewer high-central words link these communities into a highly efficient small-world network. Understanding this process may be crucial for understanding the increasing structural complexity of the language system.

Choosing an appropriate measurement unit of word length is a key prerequisite for word length distribution studies, since the measurement unit varies with different types of language or text. Taking Chinese as an example, this study explores the word length distributions of Chinese spoken and written language based on a data source consisting of 20 dialogue texts (spoken language) and 20 prose texts (written language), in which the lengths of words are variously determined in terms of pinyin letter, phoneme, syllable for spoken Chinese and stroke, component, character for written Chinese respectively. With the aim of selecting the most appropriate word length measurement unit, empirical word length distribution models, synergetic linguistic theories and Menzerath’s law are used in this study. Results show that the syllable is the most appropriate measurement unit for spoken Chinese, and the component the most appropriate measurement unit for written Chinese. Chinese word length distributions can be described with the Poisson or Binomial distribution families, among which Extended Logarithmic and Mixed Poisson are the most generally accepted models for spoken and written Chinese respectively.

We demonstrate a substantial evidence that the word length can be an essential lexical structural feature for word evolution in written Chinese. The data used in this study are diachronic Chinese short narrative texts with a time span of over 2000-years. We show that the increase of word length is an essential regularity in word evolution. On the one hand, word frequency is found to depend on word length, and their relation is in line with the Power law function y = ax^-b. On the other hand, our deeper analyses show that the increase of word length results in the simplification in characters for balance in written Chinese. Moreover, the correspondence between written and spoken Chinese is discussed. We conclude that the disyllabic trend may account for the increase of word length, and its impacts can be explained in "the principle of least effort".

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 at a corpus level. A more in-depth investigation reveals that when compared to traditional lexical complexity indices, such as average word length and hapax legomena percentage (though Guiraud proves to be a robust predictor), the lexical network indices, such as network density and network clusters, provide a more profound understanding of L2 proficiency distinctions and a more precise approximation of the target language. Moreover, our findings illuminate the consistent manifestation of complex network characteristics within L2 Chinese lexical networks across all proficiency levels. Additionally, word association 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 frequency and lexical network features to gain a comprehensive understanding of L2 lexical development.