title: Trends in syntactic parsing: anticipation, Bayesian estimation, and good-enough parsing.
abstract: Syntactic parsing processes establish dependencies between words in a sentence. These dependencies affect how comprehenders assign meaning to sentence constituents. Classical approaches to parsing describe it entirely as a bottom-up signal analysis. More recent approaches assign the comprehender a more active role, allowing the comprehender's individual experience, knowledge, and beliefs to influence his or her interpretation. This review describes developments in three related aspects of sentence processing research: anticipatory processing, Bayesian/noisy-channel approaches to sentence processing, and the 'good-enough' parsing hypothesis.
authors.affiliation: Department of Psychology, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA. Electronic address: mjtraxler@ucdavis.edu.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Traxler
mesh_terms: D059031:Anticipation, Psychological; D001499:Bayes Theorem; D032882:Comprehension; D006801:Humans; D007802:Language; D008570:Memory, Short-Term; D011578:Psycholinguistics; D012660:Semantics; D013067:Speech Perception
keywords: Bayes’ theorem; good-enough parsing; noisy channels; parsing; syntax