Sciweavers

NIPS
2001

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

13 years 5 months ago
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.
S. Narayanan, Daniel Jurafsky
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where NIPS
Authors S. Narayanan, Daniel Jurafsky
Comments (0)