Complexity Characteristics of Natural Language

This Reprint presents recent advances in the study of natural language as a complex adaptive system analyzed through the methodologies of complexity science, information theory, and statistical physics. The collected papers show how linguistic structure and dynamics emerge from interacting constraints across multiple scales, including characters, words, syntax, discourse, and language families. Contributions examine scaling laws, long-range correlations, and multifractal properties in diverse data sources such as literary texts, social media, and large comparative corpora. Several studies demonstrate that features often viewed as secondary, including punctuation and sentence-length variability, play a key role in organizing linguistic dependencies. Other works explore spatial, temporal, grammatical, and genealogical dimensions of language, revealing both universal regularities and context-specific patterns.

A central theme of the Reprint is the move beyond correlation toward causal analysis, combining information-theoretic measures with causal inference to clarify directional relationships between linguistic subsystems. The Reprint also critically engages with large language models, showing that despite surface fluency, machine-generated texts often lack essential complexity signatures found in human language.