The word-frequency distribution provides the fundamental building blocks that generate discourse in language. It is well known, from empirical evidence, that the word-frequency distribution of almost any text is described by Zipfs law, at least approximately. Following Stephens and Bialek [Phys. Rev. E 81, 066119, 2010], we interpret the frequency of any word as arising from the interaction potential between its constituent letters. Indeed, Jaynes maximum-entropy principle, with the constrains given by every empirical two-letter marginal distribution, leads to a Boltzmann distribution for word probabilities, with an energy-like function given by the sum of all pairwise (two-letter) potentials. The improved iterative-scaling algorithm allows us finding the potentials from the empirical two-letter marginals. Appling this formalism to words with up to six letters from the English subset of the recently created Standardized Project Gutenberg Corpus, we find that the model is able to reproduce Zipfs law, but with some limitations: the general Zipfs power-law regime is obtained, but the probability of individual words shows considerable scattering. In this way, a pure statistical-physics framework is used to describe the probabilities of words. As a by-product, we find that both the empirical two-letter marginal distributions and the interaction-potential distributions follow well-defined statistical laws.
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