---
title: "Identifying the Periodicity of Information in Natural Language (2026)"
description: "The authors introduce AutoPeriod of Surprisal (APS), an adaptation of the AutoPeriod time-series algorithm, to detect periods… 39 references."
authors: ["Yulin OU", "Yu Wang", "Yang Xu", "Hendrik Buschmeier"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.52/"
doi: "10.18653/v1/2026.acl-long.52"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ou-2026-identifying-periodicity-information-natural-language/"
---
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# Identifying the Periodicity of Information in Natural Language

**Authors:** Yulin OU, [Yu Wang](https://md.research.elamur.ai/free/en/authors/yu-wang/), [Yang Xu](https://md.research.elamur.ai/free/en/authors/yang-xu/) and [Hendrik Buschmeier](https://md.research.elamur.ai/free/en/authors/hendrik-buschmeier/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.52/) · [PDF](https://aclanthology.org/2026.acl-long.52.pdf) · [DOI 10.18653/v1/2026.acl-long.52](https://doi.org/10.18653/v1/2026.acl-long.52) · [OpenAlex](https://openalex.org/W4415917286)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
This paper asks whether the information carried by text, measured as token surprisal, rises and falls in genuinely periodic cycles. Earlier work offered only indirect evidence: Fourier spectra were never converted back into explicit periods, and harmonic regression tests only candidate periods tied to known units such as sentences. The authors introduce AutoPeriod of Surprisal (APS), an adaptation of the AutoPeriod time-series algorithm, to detect periods in a single document's surprisal sequence at a stated confidence level.

APS works in two stages:
- **Period hints.** Spectral peaks are kept only if they exceed a power threshold derived from random permutations of the sequence.
- **Validation.** The surviving candidates are checked against the autocorrelation function.

Applied to English and Chinese corpora, APS finds that a substantial share of human-written documents show significant periodicity. Some detected periods fall outside the typical lengths of sentences, paragraphs or EDUs, and harmonic regression confirms them. The authors therefore suggest that longer-range factors, possibly topic structure, contribute alongside structural units. LLM-generated text turns out to be more periodic than human text, which points to uses in detecting machine-generated text.

## Key points
- APS builds on AutoPeriod (Vlachos et al., 2005), using 100 permutations to set its threshold and a Lomb-Scargle periodogram to better capture long periods. Experiments use a confidence level of .90.
- In a worked example, document #0976 of PTB-WSJ (TV listings) yields its most confident period at roughly 53 tokens.
- For BBC news, LLaMA3-70B outputs have valid periods more often than human-written articles, at 27.86% versus 14.80%. This gap holds even after excluding repetitive outputs. In a 150-document sample, 11 (7.33%) contained repeated phrases or sentences.
- Stated limitations:
  - Only English and Chinese are covered.
  - Short-period detection is less robust.
  - The sources of the non-structural periods are left unexplained.
- Code is released on GitHub (CLCS-SUSTech/APS) and Zenodo.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Bielefeld University](https://md.research.elamur.ai/free/en/universities/bielefeld-university/) — University, Bielefeld, Germany · 35 works · 1,196 citations
- [Southern University of Science and Technology](https://md.research.elamur.ai/free/en/universities/southern-university-of-science-and-technology/) — University, Shenzhen, China · 19 works · 1,337 citations

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## Cite

Cite as: Yulin OU, Yu Wang, Yang Xu and Hendrik Buschmeier. 2026. Identifying the Periodicity of Information in Natural Language. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ou2026identifying,
  title = {{Identifying the Periodicity of Information in Natural Language}},
  author = {Yulin OU, Yu Wang, Yang Xu and Hendrik Buschmeier},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.52},
  url = {https://doi.org/10.18653/v1/2026.acl-long.52}
}
```

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38. Yang Xu and David Reitter. 2017. Spectral analysis of information density in dialogue predicts collaborative task performance. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 623–633, Vancouver, Canada. ACL. [find](https://scholar.google.com/scholar?q=Yang+Xu+and+David+Reitter%2E+2017%2E+Spectral+analysis+of+information+density+in+dialogue+predicts+collaborative+task+performance%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computat)
39. Yang Xu and David Reitter. 2018. Information density converges in dialogue: Towards an information-theoretic model. Cognition, 170:147–163. *Table 6: Significance test of all the amplitudes in HR models using APS hints as scaling factors on WSJ (𝑃 1 portion).* 𝑘 𝐴 𝑘 𝛽 coef 𝑃 \> \|𝑡\| 0.1772 0.0255 0.05… [find](https://scholar.google.com/scholar?q=Yang+Xu+and+David+Reitter%2E+2018%2E+Information+density+converges+in+dialogue%3A+Towards+an+information%2Dtheoretic+model%2E+Cognition%2C+170%3A147%E2%80%93163%2E+%2ATable+6%3A+Significance+test+of+all+the+amplitudes+in+HR+mode)

*Updated 1 November 2026*