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Wide-coverage probabilistic sentence processing.

M W Crocker1, T Brants

  • 1Department of Computational Linguistics, Universität des Saarlandes, Saarbücken, Germany. crocker.thorsten@coli.uni-sb.de

Journal of Psycholinguistic Research
|February 24, 2001
PubMed
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Search strategies in syntactic reanalysis.

Journal of psycholinguistic research·2000
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This study presents a computational model for human syntactic processing using probabilistic parsing. This approach accurately models typical language and explains human parsing behavior, including garden-path sentences.

Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Natural Language Processing

Background:

  • Human syntactic processing exhibits both general accuracy and occasional errors (e.g., garden-path sentences).
  • Existing models may not fully capture this dual nature of linguistic performance.

Purpose of the Study:

  • To describe a fully implemented, broad-coverage computational model of human syntactic processing.
  • To demonstrate how probabilistic parsing can explain human linguistic behavior.

Main Methods:

  • Utilized probabilistic parsing techniques combining phrase structure, lexical category, and subcategory probabilities.
  • Employed an incremental, left-to-right pruning mechanism based on cascaded Markov models.
  • Established model parameters via a uniform training algorithm using maximum-likelihood estimates from a parsed corpus.

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Main Results:

  • The probabilistic parsing model achieved good accuracy on typical language corpora.
  • The incremental probabilistic ranking of analyses naturally explained human behavior on garden-path structures.
  • The model provides a framework for understanding the generally effective yet occasionally flawed nature of human language processing.

Conclusions:

  • Incremental probabilistic parsing models are well-suited to explaining the dual characteristics of human linguistic performance.
  • The presented model offers a computational account of syntactic processing, accounting for both typical and atypical human behavior.
  • While not making strong psychological claims, the model highlights the utility of probabilistic approaches in cognitive linguistics.