Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing

Authors:
Brian Roark;Asaf Bachrach;Carlos Cardenas;Christophe Pallier
Affiliations:
Oregon Health & Science University;INSERM-CEA Cognitive Neuroimaging Unit, Gif sur Yvette, France;MIT;INSERM-CEA Cognitive Neuroimaging Unit, Gif sur Yvette, France
Venue:
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Year:
2009

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Abstract

A number of recent publications have made use of the incremental output of stochastic parsers to derive measures of high utility for psycholinguistic modeling, following the work of Hale (2001; 2003; 2006). In this paper, we present novel methods for calculating separate lexical and syntactic surprisal measures from a single incremental parser using a lexicalized PCFG. We also present an approximation to entropy measures that would otherwise be intractable to calculate for a grammar of that size. Empirical results demonstrate the utility of our methods in predicting human reading times.