| |
Abstract:
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.
References
Narayanan, S., & Jurafsky, D. (1998). Bayesian models of
human sentence processing. In
COGSCI-98
, pp. 752-757. Madison, WI. Lawrence Erlbaum.
|
