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Do neural nets learn statistical laws behind natural language?

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Related Experiment Video

Updated: Nov 3, 2025

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Menzerath's Law in the Syntax of Languages Compared with Random Sentences.

Kumiko Tanaka-Ishii1

  • 1Research Center for Advanced Technology, The University of Tokyo, Tokyo 153-8904, Japan.

Entropy (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

The Menzerath law, describing linguistic complexity, was tested across 76 languages. The study found the law holds for sentence structures with more than two modifiers, even in randomized data.

Keywords:
Menzerath lawcomplexitynatural languagesyntax

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Area of Science:

  • Linguistics
  • Computational Linguistics
  • Natural Language Processing

Background:

  • The Menzerath law posits an inverse relationship between the size of a linguistic unit and the number of constructs it contains.
  • Previous research indicated the Menzerath law's applicability to dependency corpora in languages like Czech and Ukrainian.
  • Syntactic complexity in natural language is a key area of linguistic research.

Purpose of the Study:

  • To investigate the syntactic application of the Menzerath law across a wide range of languages.
  • To determine if the Menzerath law's properties hold consistently in diverse linguistic structures.
  • To compare the Menzerath law's behavior in natural language syntax versus syntactically randomized data.

Main Methods:

  • Utilized the Universal Dependency dataset (version 2.3), encompassing 76 languages and 129 corpora.
  • Analyzed the Penn Treebank (PTB) dataset for syntactic properties.
  • Generated and analyzed syntactically randomized sentences from the PTB for comparative purposes.

Main Results:

  • The Menzerath law was found to hold reasonably well for sentences with more than two modifying constituents (x>2).
  • The observed patterns of the Menzerath law were found to be highly reproducible even in syntactically randomized sentence structures.
  • Cross-linguistic analysis confirmed the general applicability of the Menzerath law in syntactic structures.

Conclusions:

  • The Menzerath law demonstrates a consistent, albeit sometimes weak, pattern in syntactic structures across numerous languages.
  • The reproducibility of the Menzerath law in randomized data suggests that simple statistical properties may underlie some aspects of linguistic complexity.
  • Further analysis is warranted to explore the nuanced characteristics of natural language complexity illuminated by the Menzerath law.