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Structure before meaning: sentence processing, plausibility, and subcategorization.

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Summary

Syntactic parsing in natural language processing attaches fillers early, ignoring implausibility but respecting verb subcategorization. This indicates a parser guided by syntax, not semantics.

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Area of Science:

  • Computational Linguistics
  • Psycholinguistics
  • Cognitive Science

Background:

  • Parsing is key to natural language processing (NLP).
  • Online incremental syntactic structure construction is vital.
  • Understanding filler-object attachment in embedded clauses is crucial.

Purpose of the Study:

  • To determine if wh-fillers attach to matrix verbs early.
  • To investigate the effect of attachment plausibility on reaction time.
  • To examine the role of subcategorization in parsing.

Main Methods:

  • Self-paced reading design using the G-Maze method.
  • Measurement of response time.
  • Analysis of sentence acceptability and reaction times.

Main Results:

  • Confirmed early attachment of fillers to the matrix verb.
  • Implausible attachments significantly reduced off-line acceptability.
  • Parser ignores implausibility but uses subcategorization frames.
  • G-Maze proved effective for online parsing experiments.

Conclusions:

  • The parser prioritizes early attachment based on syntactic cues.
  • Semantic implausibility is disregarded, but syntactic constraints are followed.
  • Evidence supports a syntax-informed parsing model over semantic or autonomous models.