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Word learning under infinite uncertainty.

Richard A Blythe1, Andrew D M Smith2, Kenny Smith3

  • 1SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3JZ, UK.

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|March 2, 2016
PubMed
Summary
This summary is machine-generated.

Learning new words is challenging due to infinite referential uncertainty. This study shows that even weak plausibility rankings enable word learning, suggesting research into probabilistic constraints for language acquisition.

Keywords:
Cross-situational learningQuine’s ProblemWord learning

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Area of Science:

  • Cognitive Science
  • Linguistics
  • Computational Linguistics

Background:

  • Language acquisition involves learning word meanings from contextual clues.
  • Infinite referential uncertainty, as described by Quine, poses a significant challenge to word learning.
  • Previous models often rely on strong, innate constraints for successful word learning.

Purpose of the Study:

  • To mathematically formalize an ideal cross-situational learner facing infinite referential uncertainty.
  • To identify the conditions necessary for successful word learning under such uncertainty.
  • To investigate the role and potential weakness of constraints in inferring word meaning.

Main Methods:

  • Developed a mathematical model of an ideal cross-situational learner.
  • Analyzed the conditions for word learning under infinite referential uncertainty.
  • Investigated the impact of plausibility ranking on learning outcomes.

Main Results:

  • Word learning is possible even with infinite referential uncertainty.
  • A weak ranking of candidate word meanings is sufficient for learning.
  • The effectiveness of learning does not necessitate strong, 'smart' constraints.

Conclusions:

  • The burden of explanation can be shifted from 'smart' learner constraints to weaker, probabilistic ones.
  • Research should focus on identifying weak, unreliable constraints in real-world language learners.
  • This formalization provides a new perspective on the fundamental problem of word meaning inference.