A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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S. Narayanan, Daniel Jurafsky


Narayanan and Jurafsky (1998) proposed that human language compre- hension can be modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension process with Bayesian de- cision trees. In this paper we extend the Narayanan and Jurafsky model to make further predictions about reading time given the probability of difference parses or interpretations, and test the model against reading time data from a psycholinguistic experiment.