Cognitive scale-free networks as a model for intermittency in human natural language

TL;DR

This paper models human language complexity using a scale-free network approach, demonstrating how a random walk on such networks explains linguistic phenomena like Zipf's law, supported by empirical analysis of Italian texts.

Contribution

It introduces a novel model linking scale-free networks to language complexity and validates it with empirical data from Italian language corpora.

Findings

The complexity of Italian language texts aligns with a random walk on a scale-free network.

The model reproduces Zipf's law through the generalized central limit theorem.

Empirical analysis supports the network-based dynamical model of language.

Abstract

We model certain features of human language complexity by means of advanced concepts borrowed from statistical mechanics. Using a time series approach, the diffusion entropy method (DE), we compute the complexity of an Italian corpus of newspapers and magazines. We find that the anomalous scaling index is compatible with a simple dynamical model, a random walk on a complex scale-free network, which is linguistically related to Saussurre's paradigms. The model yields the famous Zipf's law in terms of the generalized central limit theorem.