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Related Experiment Videos

Learning first-pass structural attachment preferences with dynamic grammars and recursive neural networks.

Patrick Sturt1, Fabrizio Costa, Vincenzo Lombardo

  • 1Department of Psychology, University of Glasgow, 58 Hillhead Street, Glasgow, G12 8QB, UK. patrick@psy.gla.ac.uk

Cognition
|May 24, 2003
PubMed
Summary

This study presents a computational model for human language ambiguity resolution. The model learns from linguistic experience to successfully interpret ambiguous sentences, mimicking human cognitive processes.

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Psycholinguistics

Background:

  • Human language processing faces significant challenges in resolving pervasive ambiguity.
  • Experience-based models propose that past successful outcomes guide ambiguity resolution.
  • Generalizing from past linguistic experiences is crucial for effective ambiguity resolution.

Purpose of the Study:

  • To present a computational experience-based model for human language ambiguity resolution.
  • To demonstrate how models can learn to generalize over linguistic experience from syntactic structures.
  • To investigate the role of symbolic grammars and neural networks in processing linguistic ambiguity.

Main Methods:

  • Developed a hybrid computational model combining symbolic grammars and neural networks.

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  • Utilized a dynamic grammar for tight correspondence between derivations and incremental processing.
  • Employed recursive neural networks to handle complex hierarchical syntactic structures.

    • Main Results:

    • The model successfully reproduces key structural preferences observed in psycholinguistic experiments.
    • The model demonstrates robust performance on unrestricted text, indicating generalizability.
    • The hybrid approach effectively ranks syntactic structures based on learned experience.

    Conclusions:

    • Computational models can effectively learn to resolve linguistic ambiguity through experience.
    • Hybrid systems integrating symbolic and neural approaches show promise for language processing.
    • The findings support experience-based theories of human language comprehension.