Probabilistic constraints and syntactic ambiguity resolution

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Abstract
Natural languages contain probabilistic constraints that influence the resolution of ambiguities. Current models of sentence processing agree that probabilistic constraints affect syntactic ambiguity resolution, but there has been little investigation of the constraints themselves-what they are, how they differ in their effects on processing, and how they interact with one another. Three different types of probabilistic constraints were investigated: “pre-ambiguity” plausibility information, information about verb argument structure frequencies, and “post-ambiguity” constraints that arrive after the introduction of the ambiguity but prior to its disambiguation. Reading times for syntactically ambiguous sentences were compared to reading times for unambiguous controls in three self-paced reading experiments. All three kinds of constraints were found to be helpful, and when several constraints converged, ambiguity resolution was facilitated compared to when constraints conflicted. The importance of these constraint interactions for ambiguity resolution models is discussed.