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

Narayanan and Jurafsky (1998) proposed that human language comprehension can be modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension process with Bayesian decision 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.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: S. Narayanan, Daniel Jurafsky
• Keywords: Bayesian Decision Trees, Human Language Comprehension, Jurafsky Model, NIPS 2001, NIPS 2007