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Parsing Complex Sentences with Structured Connectionist Networks.

Ajay N Jain1

  • 1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 USA.

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This summary is machine-generated.

This study introduces a connectionist network that learns to parse complex sentences word by word. It achieves semantic role assignment and syntactic analysis, demonstrating robust generalization and tolerance for imperfect input.

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Area of Science:

  • Computational Linguistics
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Natural Language Processing (NLP) challenges in parsing complex sentence structures.
  • Need for models that can incrementally interpret linguistic input.
  • Limitations of traditional rule-based parsing systems.

Purpose of the Study:

  • To develop and evaluate a modular, recurrent connectionist network for incremental sentence parsing.
  • To enable the network to perform semantic role assignment, noun phrase attachment, and clause structure recognition.
  • To investigate the network's ability to dynamically infer grammar rules and generalize.

Main Methods:

  • Training a recurrent connectionist network on sequential word input.
  • Implementing mechanisms for real-time syntactic and semantic prediction and revision.
  • Utilizing a modular architecture for handling complex grammatical constructions.

Main Results:

  • The network successfully performed incremental parsing, including semantic role assignment and syntactic analysis.
  • Demonstrated accurate processing of active, passive, and center-embedded clauses.
  • Showcased the ability to revise predictions based on new information and generalize to novel sentences.

Conclusions:

  • Recurrent connectionist networks can effectively learn incremental sentence parsing.
  • The developed model exhibits adaptive learning and robust performance on complex linguistic data.
  • This approach offers a promising direction for computational models of language comprehension.