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Ease of learning explains semantic universals.

Shane Steinert-Threlkeld1, Jakub Szymanik2

  • 1Department of Linguistics, University of Washington, United States.

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Summary
This summary is machine-generated.

Semantic universals, or shared meaning properties across languages, are explained by their simplicity and ease of learning. This study uses machine learning to show that simpler expressions, aligning with universals, are easier to acquire.

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Area of Science:

  • Linguistics
  • Cognitive Science
  • Computational Linguistics

Background:

  • Semantic universals represent meaning properties common to all world languages.
  • Existing explanations for semantic universals lack a unified, empirical basis.
  • The relationship between linguistic universals and cognitive processing remains underexplored.

Purpose of the Study:

  • To propose and test an explanation for semantic universals based on learning simplicity.
  • To empirically measure the ease of learning for expressions that adhere to or violate semantic universals.
  • To investigate whether simplicity, defined by ease of learning, underlies semantic universals in both function and content words.

Main Methods:

  • Utilized machine learning models to quantify the ease of learning for linguistic expressions.
  • Analyzed semantic universals within the domains of quantifiers (function words) and color terms (content words).
  • Compared learning difficulty metrics for expressions conforming to universals versus those that do not.

Main Results:

  • Expressions satisfying semantic universals were found to be significantly simpler to learn.
  • This simplicity-by-ease-of-learning criterion held true for both function words (quantifiers) and content words (color terms).
  • Machine learning analysis provided robust evidence supporting the learning simplicity hypothesis for semantic universals.

Conclusions:

  • Semantic universals across diverse word classes reflect underlying principles of learning simplicity.
  • Ease of learning provides a parsimonious explanation for the presence of meaning properties shared by the world's languages.
  • This finding bridges linguistic typology and cognitive processing, offering a computational perspective on language acquisition and universals.