ArticlePloS one2016
Large-Scale Analysis of Zipf's Law in English Texts.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Scaling Laws in Language Families.Entropy (Basel, Switzerland) · 2025Article
- Zipf's law in China's local government work reports: A 21-year study using natural language processing and regression analysis.PloS one · 2025Article
- Word Embeddings as Statistical Estimators.Sankhya. Series B. [Methodological.] · 2024Article
- Quantitative Aspects of the Human Cell Proteome.International journal of molecular sciences · 2023Review
- Zipf's law revisited: Spoken dialog, linguistic units, parameters, and the principle of least effort.Psychonomic bulletin & review · 2023Review
- Zipfian Distributions in Child-Directed Speech.Open mind : discoveries in cognitive science · 2023Article
- Drift as a Driver of Language Change: An Artificial Language Experiment.Cognitive science · 2022Article
- Lognormals, power laws and double power laws in the distribution of frequencies of harmonic codewords from classical music.Scientific reports · 2022Article
- Linguistic laws in biology.Trends in ecology & evolution · 2022Review
- Bias in Zipf's law estimators.Scientific reports · 2021Article
- True scale-free networks hidden by finite size effects.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- The Brevity Law as a Scaling Law, and a Possible Origin of Zipf's Law for Word Frequencies.Entropy (Basel, Switzerland) · 2020Article
- From Boltzmann to Zipf through Shannon and Jaynes.Entropy (Basel, Switzerland) · 2020Article
- A Standardized Project Gutenberg Corpus for Statistical Analysis of Natural Language and Quantitative Linguistics.Entropy (Basel, Switzerland) · 2020Article
- On the authenticity of COVID-19 case figures.PloS one · 2020Article
- Dynamical analogues of rank distributions.PloS one · 2019Article
- A power law distribution of metabolite abundance levels in mice regardless of the time and spatial scale of analysis.Scientific reports · 2018Article
- Emergence of linguistic laws in human voice.Scientific reports · 2017Article
- Size distribution of function-based human gene sets and the split-merge model.Royal Society open science · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite being a paradigm of quantitative linguistics, Zipf's law for words suffers from three main problems: its formulation is ambiguous, its validity has not been tested rigorously from a statistical point of view, and it has not been confronted to a representatively large number of texts. So, we can summarize the current support of Zipf's law in texts as anecdotic. We try to solve these issues by studying three different versions of Zipf's law and fitting them to all available English texts in the Project Gutenberg database (consisting of more than 30 000 texts). To do so we use state-of-the art tools in fitting and goodness-of-fit tests, carefully tailored to the peculiarities of text statistics. Remarkably, one of the three versions of Zipf's law, consisting of a pure power-law form in the complementary cumulative distribution function of word frequencies, is able to fit more than 40% of the texts in the database (at the 0.05 significance level), for the whole domain of frequencies (from 1 to the maximum value), and with only one free parameter (the exponent).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.