Abstract

Word lengths are not optimized for efficient communication

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Abstract

Zipf's law of abbreviation, a negative correlation between word length and frequency, has been shown to apply in many languages, and is widely regarded as a linguistic universal. However, it has also been shown that word length correlates strongly with surprisal, or average information content. The relative strengths of these correlations have been used as an argument that lexicons are somehow optimized for efficient communication: Words are short because they are frequent or highly informative, implying a causal relationship between word length and frequency or average surprisal. While previous work implies the existence of these causal relationships, they have not explicitly been tested for. In this paper, we apply causal discovery algorithms to lexical data from 12 languages, and find that there is no universal causal relationship between word length, frequency, and average surprisal. Instead, languages vary in the causal structure of their lexicons, suggesting that while Zipf's law describes a universal correlation across languages, that correlation is not the result of a universal causal process.

Keywords: zipf, causal inference, causal model, surprisal, frequency

How to Cite:

Doucette, A., O'Donnell, T. J. & Sonderegger, M., (2026) “Word lengths are not optimized for efficient communication”, Society for Computation in Linguistics 9(1). doi: https://doi.org/10.7275/scil.4039