%0 Generic %1 NarayananJurafsky2002 %A Narayanan, Srini %A Jurafsky, Daniel %D 2002 %K article bayesiannetworks cognitivemodeling sentenceprocessing %T A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing %X 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.

@misc{NarayananJurafsky2002, 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. }, added-at = {2007-06-09T21:51:11.000+0200}, author = {Narayanan, Srini and Jurafsky, Daniel}, biburl = {https://www.bibsonomy.org/bibtex/2b7c34ca6c508eca3dc9472f4ca67b3a1/tmalsburg}, interhash = {d6e44b08e62b976e24467c938e620aa3}, intrahash = {b7c34ca6c508eca3dc9472f4ca67b3a1}, keywords = {article bayesiannetworks cognitivemodeling sentenceprocessing}, timestamp = {2007-06-09T21:51:11.000+0200}, title = {A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing}, year = 2002 }