Paper: Lexical surprisal as a general predictor of reading time

ACL ID E12-1041
Title Lexical surprisal as a general predictor of reading time
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Probabilistic accounts of language process- ing can be psychologically tested by com- paring word-reading times (RT) to the con- ditional word probabilities estimated by language models. Using surprisal as a link- ing function, a significant correlation be- tween unlexicalized surprisal and RT has been reported (e.g., Demberg and Keller, 2008), but success using lexicalized models has been limited. In this study, phrase struc- ture grammars and recurrent neural net- works estimated both lexicalized and unlex- icalized surprisal for words of independent sentences from narrative sources. These same sentences were used as stimuli in a self-paced reading experiment to obtain RTs. The results show that lexicalized sur- prisal according to both models is a signif- icant predictor of RT, outperfo...