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A Bayesian Approach to German Personal and Demonstrative Pronouns.

Clare Patterson1, Petra B Schumacher1, Bruno Nicenboim2

  • 1Department of German Language and Literature I, Linguistics, University of Cologne, Cologne, Germany.

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|March 21, 2022
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This summary is machine-generated.

This study validates the Bayesian model for pronoun interpretation in German. The model accurately predicts how people resolve ambiguous pronouns, outperforming other biases.

Keywords:
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Area of Science:

  • Psycholinguistics
  • Computational Linguistics
  • Cognitive Science

Background:

  • Pronoun interpretation relies on identifying referents, often modeled using Bayes' Rule.
  • Previous research successfully applied Bayesian models to English and Mandarin Chinese.

Purpose of the Study:

  • To extend cross-linguistic evidence for the Bayesian pronoun interpretation model.
  • To apply and quantitatively assess the model using German personal and demonstrative pronouns.
  • To provide conservative uncertainty estimates via a hierarchical Bayesian framework.

Main Methods:

  • Two story-continuation experiments were conducted with German speakers.
  • Model performance was evaluated within a hierarchical Bayesian statistical framework.
  • Predictions were compared against production and next-mention biases.

Main Results:

  • The Bayesian model demonstrated superior accuracy in predicting pronoun interpretation compared to alternative biases.
  • The model successfully accounted for both personal and demonstrative pronoun resolution in German.
  • Demonstrative pronoun resolution preferences, though rigid, were captured by the model.

Conclusions:

  • The Bayesian framework provides a robust and accurate account of pronoun interpretation across languages.
  • The study strengthens the cross-linguistic validity of the Bayesian approach to psycholinguistics.
  • Hierarchical Bayesian modeling offers a conservative method for estimating uncertainty in cognitive models.