  Taylor's law describes the fluctuation characteristics underlying a system in
which the variance of an event within a time span grows by a power law with
respect to the mean. Although Taylor's law has been applied in many natural and
social systems, its application for language has been scarce. This article
describes a new quantification of Taylor's law in natural language and reports
an analysis of over 1100 texts across 14 languages. The Taylor exponents of
written natural language texts were found to exhibit almost the same value. The
exponent was also compared for other language-related data, such as the
child-directed speech, music, and programming language code. The results show
how the Taylor exponent serves to quantify the fundamental structural
complexity underlying linguistic time series. The article also shows the
applicability of these findings in evaluating language models.
