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Conceptualization in reference production: Probabilistic modeling and experimental testing.

Roger P G van Gompel1, Kees van Deemter2, Albert Gatt3

  • 1School of Social Sciences.

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Summary
This summary is machine-generated.

Speakers choose object descriptions by first selecting a property to uniquely identify an item. They may add a second property, with the probabilistic referential overspecification (PRO) model best explaining these choices.

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Area of Science:

  • Psycholinguistics
  • Artificial Intelligence
  • Natural Language Generation

Background:

  • Limited research exists on conceptualization in psycholinguistics, specifically how speakers select concepts for expression.
  • Natural Language Generation (NLG) research in AI has extensively studied content determination for referring expressions.

Purpose of the Study:

  • To develop and test new models of conceptualization in language production.
  • To explain psycholinguistic findings on object description using computational models.

Main Methods:

  • Three language production experiments were conducted where participants described target objects.
  • Models were tested against the distribution of description types (e.g., 'small gray candle') produced by participants.

Main Results:

  • The probabilistic referential overspecification (PRO) model demonstrated the best fit with experimental data.
  • The PRO model accounts for the selection of unique properties and the probabilistic addition of overspecified properties.

Conclusions:

  • The PRO model offers a robust explanation for conceptualization strategies in object description.
  • This research bridges psycholinguistic findings with NLG models for content determination.