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Related Experiment Videos

Connectionist natural language parsing.

Dominic Palmer-Brown1, Jonathan A. Tepper, Heather M. Powell

  • 1Leeds Metropolitan University, Computational Intelligence Research Group, School of Computing, Beckett Park, LS6 3QS, Leeds, UK

Trends in Cognitive Sciences
|November 5, 2002
PubMed
Summary
This summary is machine-generated.

This review examines connectionist parsing, focusing on automatic syntactic structure learning and its role as a computational model for human sentence processing and psycholinguistic data. It assesses parsers

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Connectionist parsing models aim to learn syntactic structures automatically from data, bypassing explicit symbolic grammar rules.
  • Two decades of research have advanced these models, exploring their potential in natural language processing.

Purpose of the Study:

  • To review key developments in connectionist parsing over the past twenty years.
  • To assess connectionist parsers' ability for automatic syntactic structure representation.
  • To evaluate connectionist parsers as computational models of human sentence processing and psycholinguistic data.

Main Methods:

  • Systematic review of connectionist parsing literature.
  • Assessment of parsers based on learning capabilities, computational modeling, and psycholinguistic plausibility.
  • Analysis of realism, modularity, and processing types across various connectionist parsers.

Main Results:

  • Connectionist parsers demonstrate varying degrees of success in learning syntactic structures without explicit rules.
  • The models offer insights into human sentence processing, with some providing plausible accounts of psycholinguistic findings.
  • Key considerations include the level of realism, modularity, and processing mechanisms employed.

Conclusions:

  • Connectionist parsing has evolved significantly, offering valuable computational models for language acquisition and processing.
  • Further research is needed to enhance realism and align models more closely with human cognitive processes.