# Taylor's law for Human Linguistic Sequences

arXiv: https://arxiv.org/abs/1804.07893 · HTML: https://mlsanity.com/paper/1804.07893/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [SoftMatcha: A Soft and Fast Pattern Matcher for Billion-Scale Corpus Searches](https://mlsanity.com/paper/2503.03703/) (2025)
- [From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis](https://mlsanity.com/paper/2512.13491/) (2025)
- [Correlation Dimension of Natural Language in a Statistical Manifold](https://mlsanity.com/paper/2405.06321/) (2024)
- [Strahler Number of Natural Language Sentences in Comparison with Random Trees](https://mlsanity.com/paper/2307.02697/) (2023)
- [Scale-free Characteristics of Multilingual Legal Texts and the Limitations of LLMs](https://mlsanity.com/paper/2509.17367/) (2025)

## Abstract (arXiv)

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.