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Related Experiment Video

Updated: May 12, 2026

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
08:32

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Published on: September 5, 2019

Probabilistic modeling of discourse-aware sentence processing.

Amit Dubey1, Frank Keller, Patrick Sturt

  • 1Google Inc. amit@dubey.ca

Topics in Cognitive Science
|April 26, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces two novel sentence processing models that integrate syntax with discourse co-reference information. These enhanced probabilistic models more accurately reflect human language comprehension by considering broader linguistic factors.

Keywords:
Co-reference resolutionCognitive modelingDiscourse/syntax interactionsMarkov logicSentence processing

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Psycholinguistics

Background:

  • Probabilistic models are key to understanding human language processing.
  • Current models often focus narrowly on syntax, neglecting other linguistic influences.
  • Experimental data show syntax interacts with other information types during sentence comprehension.

Purpose of the Study:

  • To develop advanced sentence processing models.
  • To integrate syntactic information with discourse co-reference.
  • To create models that better simulate human sentence comprehension.

Main Methods:

  • Developed two novel sentence processing models.
  • Augmented a probabilistic syntactic component with co-reference classifiers.
  • Model 1: Deep linguistic model using probabilistic logic for qualitative predictions.
  • Model 2: Shallow processing for quantitative predictions on reading-time data.

Main Results:

  • The novel models demonstrated improved mimicry of human sentence processing behavior.
  • Qualitative predictions were made using the deep linguistic model.
  • Quantitative predictions were generated using the shallow processing model on a large corpus.

Conclusions:

  • Integrating discourse co-reference with probabilistic syntax significantly enhances model accuracy.
  • These models offer a more realistic approach to simulating human language comprehension.
  • The developed models provide valuable tools for studying sentence processing.