Combining Syntax and Thematic Fit in a Probabilistic Model of Sentence Processing

Pado Ulrike, Matthew W. Crocker, Frank Keller

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a model of human sentence processing that extends a standard probabilistic grammar model with a semantic module which computes the thematic t of verbs and arguments in a cognitively plausible way. Our model differs from existing probabilistic accounts (e.g., Jurafsky, 1996) by capturing both syntactic and semantic inuences in human sentence processing. It also overcomes limitations of constraint-based models (Spivey-Knowlton, 1996; Narayanan and Jurafsky, 2002), as its parameters can be acquired automatically from corpus data, and no hand-coding of constraints is required. We evaluate
our semantic module against human ratings of thematict,and also test the complete model's performance for two wellstudied ambiguities from the sentence processing literature.
Original languageEnglish
Title of host publicationProceedings of the 28th Annual Conference of the Cognitive Science Society
Number of pages6
Publication statusPublished - 2006

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