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Related Experiment Video

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Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
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Published on: May 9, 2019

A probabilistic model of semantic plausibility in sentence processing.

Ulrike Padó1, Matthew W Crocker, Frank Keller

  • 1Computational Linguistics, Saarland University School of Informatics, University of Edinburgh.

Cognitive Science
|May 19, 2011
PubMed
Summary

This study introduces a computational model for sentence processing that integrates semantic plausibility with lexico-syntactic factors. The model accurately predicts human judgments and reading times, enhancing natural language understanding research.

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Area of Science:

  • Computational linguistics
  • Cognitive science
  • Natural language processing

Background:

  • Human sentence processing involves lexical, syntactic, and semantic information.
  • Existing computational models primarily focus on lexico-syntactic factors.
  • A gap exists in computational models for general human semantic plausibility intuitions.

Purpose of the Study:

  • To develop a wide-coverage probabilistic model for human sentence processing.
  • To integrate semantic plausibility with lexico-syntactic analysis.
  • To improve computational models of natural language understanding.

Main Methods:

  • Developed a probabilistic model assigning thematic roles and evaluating (verb, role, argument) plausibility.
  • Trained the model on a corpus of role-annotated language data.
  • Integrated the semantic model with an incremental probabilistic parser.

Main Results:

  • The semantic plausibility model predicts experimental judgment and reading time data.
  • The combined syntax/semantics model also successfully predicts human data.
  • Demonstrated the model's effectiveness in capturing sentence processing nuances.

Conclusions:

  • The proposed model offers a more comprehensive approach to human sentence processing.
  • Integrating semantic plausibility significantly enhances computational linguistic models.
  • This work advances the understanding of how humans process language.