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Decomposing unaccusativity: a statistical modelling approach.

Songhee Kim1, Jeffrey R Binder1, Colin Humphries1

  • 1Department of Neurology, Medical College of Wisconsin, Milwaukee, USA.

Language, Cognition and Neuroscience
|October 31, 2024
PubMed
Summary
This summary is machine-generated.

This study proposes an experiential model for classifying intransitive verbs (unaccusative and unergative). Graded, embodied features, rather than binary properties, best explain verb distinctions.

Keywords:
distributional semanticsembodied cognitionexperiential modelstatistical modellingunaccusativity

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Area of Science:

  • Linguistics
  • Cognitive Science
  • Neurobiology

Background:

  • The classification of intransitive verbs into unergative and unaccusative types is traditionally viewed through syntactic or semantic lenses, often using binary properties like agentivity and telicity.
  • Previous research has relied on categorical intuition for verb classification, potentially overlooking nuanced distinctions.

Purpose of the Study:

  • To investigate the role of graded, embodied features rooted in neurobiological systems in distinguishing between unaccusative and unergative verbs.
  • To propose and evaluate an experiential model of verb meaning against other theoretical frameworks.

Main Methods:

  • Assessed the degree of unaccusativity using the acceptability of the prenominal past participle construction, a diagnostic test.
  • Developed and compared five models: categorical syntactic/semantic, feature-based event-semantic, experiential, and distributional models.

Main Results:

  • The experiential model demonstrated the best fit for the diagnostic test data, outperforming categorical and other feature-based models.
  • This suggests that the unaccusative/unergative distinction may emerge from underlying differences in experiential content.

Conclusions:

  • The experiential model offers a more robust explanation for verb classification, emphasizing graded, embodied features over binary semantic properties.
  • The model's advantages in interpretability and scalability warrant further exploration in linguistic and cognitive research.