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The origins of Zipf's meaning‐frequency law

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  • Ramon Ferrer‐i‐Cancho
  • Michael S. Vitevitch

Abstract

In his pioneering research, G.K. Zipf observed that more frequent words tend to have more meanings, and showed that the number of meanings of a word grows as the square root of its frequency. He derived this relationship from two assumptions: that words follow Zipf's law for word frequencies (a power law dependency between frequency and rank) and Zipf's law of meaning distribution (a power law dependency between number of meanings and rank). Here we show that a single assumption on the joint probability of a word and a meaning suffices to infer Zipf's meaning‐frequency law or relaxed versions. Interestingly, this assumption can be justified as the outcome of a biased random walk in the process of mental exploration.

Suggested Citation

  • Ramon Ferrer‐i‐Cancho & Michael S. Vitevitch, 2018. "The origins of Zipf's meaning‐frequency law," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(11), pages 1369-1379, November.
    • Handle: RePEc:bla:jinfst:v:69:y:2018:i:11:p:1369-1379
    DOI: 10.1002/asi.24057
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    Cited by:

    1. Shih-ping Wang & Wen-Ta Tseng & Robert Johanson, 2021. "To We or Not to We: Corpus-Based Research on First-Person Pronoun Use in Abstracts and Conclusions," SAGE Open, , vol. 11(2), pages 21582440211, April.
    2. Duarte-López, Ariel & Pérez-Casany, Marta & Valero, Jordi, 2020. "The Zipf–Poisson-stopped-sum distribution with an application for modeling the degree sequence of social networks," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).

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