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Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

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Published on: February 8, 2019

Distributional learning of appearance.

Lewis D Griffin1, M Husni Wahab, Andrew J Newell

  • 1Computer Science, University College London, London, United Kingdom. L.Griffin@cs.ucl.ac.uk

Plos One
|March 6, 2013
PubMed
Summary
This summary is machine-generated.

Children may learn word meanings through distributional learning, analyzing word co-occurrence patterns. This study shows appearance similarities can also be learned distributionally, aiding object identification.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Direct associationist learning of word meaning is insufficient for rapid language acquisition in children.
  • Distributional learning, using word co-occurrence statistics, is a viable method for learning word meaning.
  • The Distributional Principle suggests words with similar meanings appear in similar contexts.

Purpose of the Study:

  • To investigate if appearance similarities between words can be learned through a distributional mode.
  • To test the Appearance Hypothesis: words with similar-looking referents occur in similar contexts.
  • To evaluate a computational system's ability to identify and name unknown objects based on learned appearance and distributional similarities.

Main Methods:

  • A computational system was developed to interpolate appearance information for unknown words.
  • Appearance information was modeled using image sets for 660 concrete noun words.
  • Distributional similarity was calculated from a natural language corpus.

Main Results:

  • Computational results support the viability of distributional learning for appearance similarities.
  • The system demonstrated an ability to identify and name objects based on learned patterns.
  • Both distributional and appearance similarity contributed to the learning process.

Conclusions:

  • Distributional learning is a plausible mechanism for acquiring knowledge about word appearance, complementing meaning acquisition.
  • This approach has implications for understanding child language acquisition and developing AI systems for object recognition and naming.