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Related Experiment Video

Updated: Apr 12, 2026

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese
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Maximum entropy, word-frequency, Chinese characters, and multiple meanings.

Xiaoyong Yan1, Petter Minnhagen2

  • 1Systems Science Institute, Beijing Jiaotong University, Beijing 100044, China; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China.

Plos One
|May 9, 2015
PubMed
Summary
This summary is machine-generated.

The random group formation (RGF) model accurately predicts word frequency distributions in texts, including those in Chinese characters. This maximum entropy model uses three parameters and reveals insights into language structure and character meaning.

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Area of Science:

  • Computational Linguistics
  • Statistical Natural Language Processing
  • Information Theory

Background:

  • Word frequency distributions in texts follow predictable patterns.
  • The random group formation (RGF) model, a maximum entropy distribution, effectively describes these patterns using three parameters: total words (M), distinct words (N), and most frequent word repetitions (k(max)).

Purpose of the Study:

  • To test the applicability of the RGF-prediction to texts written in Chinese characters.
  • To investigate whether the RGF model can account for differences in frequency distributions between Chinese words and characters.
  • To explore systematic deviations from the RGF model as indicators of language-specific characteristics, such as polysemy in Chinese.

Main Methods:

  • Applying the RGF-prediction model to texts written in Chinese characters and Chinese words.
  • Analyzing frequency distributions based on the three core RGF parameters (M, N, k(max)).
  • Investigating systematic deviations from the RGF model using statistical information theory and an extended RGF model.

Main Results:

  • The RGF-prediction successfully describes the word-frequency distribution for texts in Chinese characters, similar to other languages.
  • Texts in Chinese characters and Chinese words exhibit different distribution shapes but are both well-predicted by their respective RGF parameters.
  • A systematic deviation from the RGF model was identified in Chinese, proposed to be caused by the multiple meanings of Chinese characters, with a stronger effect for characters than words.

Conclusions:

  • The RGF-prediction is a robust model for word-frequency distributions across different writing systems, including Chinese.
  • Deviations from the RGF model provide valuable insights into language-specific features, such as the polysemy of Chinese characters.
  • The study highlights the power of statistical models in uncovering linguistic properties and their relationship to information theory.