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The Unification Space implemented as a localist neural net: predictions and error-tolerance in a constraint-based
Cognitive Neurodynamics
|September 29, 2009
Summary
We developed a novel neural network model simulating human syntactic parsing. This computer implementation of the Unification-Space parser demonstrates key aspects of language processing, including reanalysis and predictive parsing.
Area of Science:
- Computational Linguistics
- Cognitive Science
- Artificial Intelligence
Background:
- Human syntactic parsing involves complex cognitive processes.
- Existing computational models may not fully capture dynamic aspects of parsing.
- The Unification-Space parser provides a theoretical framework for analyzing sentence structure.
Purpose of the Study:
- To introduce a novel computer implementation of the Unification-Space parser.
- To model human syntactic parsing using a localist neural network.
- To simulate dynamic aspects of language processing.
Main Methods:
- Developed a localist neural network based on interactive activation and inhibition.
- Utilized Performance Grammar for network wiring and feature unification.
- Represented parse trees as activation patterns during incremental input processing.
Main Results:
- The system qualitatively simulates garden-path phenomena and reanalysis.
- It models effects of syntactic complexity and fault-tolerance.
- Demonstrates rudimentary predictive parsing capabilities, including surprisal effects.
Conclusions:
- The neural network implementation offers a viable model for studying syntactic parsing.
- The system captures dynamic and cognitive aspects of human language processing.
- This approach provides insights into computational and cognitive mechanisms of sentence comprehension.
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