  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.
