Entropy rate estimates for natural language-A new extrapolation of compressed large-scale corpora

Abstract

One of the fundamental questions about human language is whether its entropy rate is positive. The entropy rate measures the average amount of information communicated per unit time. The question about the entropy of language dates back to experiments by Shannon in 1951, but in 1990 Hilberg raised doubt regarding a correct interpretation of these experiments. This article provides an in-depth empirical analysis, using 20 corpora of up to 7.8 gigabytes across six languages (English, French, Russian, Korean, Chinese, and Japanese), to conclude that the entropy rate is positive. To obtain the estimates for data length tending to infinity, we use an extrapolation function given by an ansatz. Whereas some ansatzes were proposed previously, here we use a new stretched exponential extrapolation function that has a smaller error of fit. Thus, we conclude that the entropy rates of human languages are positive but approximately 20% smaller than without extrapolation. Although the entropy rate estimates depend on the script kind, the exponent of the ansatz function turns out to be constant across different languages and governs the complexity of natural language in general. In other words, in spite of typological differences, all languages seem equally hard to learn, which partly confirms Hilberg's hypothesis.