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Quantifying Structural and Non-structural Expectations in Relative Clause Processing.

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This study integrates syntactic and non-syntactic information to model sentence processing difficulty. It uses probabilistic grammars to show how expectations, like noun phrase animacy, affect comprehension difficulty.

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Area of Science:

  • Computational Linguistics
  • Psycholinguistics
  • Cognitive Science

Background:

  • Information-theoretic complexity metrics like Surprisal and Entropy Reduction link sentence expectations to processing difficulty.
  • Current models often focus on syntactic information, but sentence processing involves broader linguistic factors.

Purpose of the Study:

  • To develop unified models of word-by-word sentence processing difficulty by combining structural and non-structural information.
  • To extend expectation-based models using probabilistic minimalist grammars and noun phrase animacy frequency.

Main Methods:

  • Utilized probabilistic minimalist grammars to model sentence processing.
  • Incorporated frequency information about noun phrase animacy into expectation-based models.
  • Analyzed entropy reductions to assess processing difficulty.

Main Results:

  • Entropy reductions accurately reflected subject-object relative clause asymmetry.
  • Animacy effects on processing difficulty profiles were successfully modeled.
  • Visualizing parser state probabilities offered new interpretations of processing asymmetries.

Conclusions:

  • Unified models integrating syntactic and non-syntactic information provide a more comprehensive account of sentence processing.
  • Expectations about arguments, including animacy, play a crucial role in processing relative clauses.
  • Probabilistic grammars offer a powerful framework for exploring linguistic complexity and processing.