# Explaining Zipf's Law via Mental Lexicon

arXiv: https://arxiv.org/abs/1302.4383 · HTML: https://mlsanity.com/paper/1302.4383/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2013.

## Built on by (most cited first, 2 of 2)

- [Optimal alphabet for single text compression](https://mlsanity.com/paper/2201.05234/) (2022)
- [Unsupervised extraction of local and global keywords from a single text](https://mlsanity.com/paper/2307.14005/) (2023)

## Abstract (arXiv)

The Zipf's law is the major regularity of statistical linguistics that served as a prototype for rank-frequency relations and scaling laws in natural sciences. Here we show that the Zipf's law -- together with its applicability for a single text and its generalizations to high and low frequencies including hapax legomena -- can be derived from assuming that the words are drawn into the text with random probabilities. Their apriori density relates, via the Bayesian statistics, to general features of the mental lexicon of the author who produced the text.