title: A model of language processing as hierarchic sequential prediction.
abstract: Computational models of memory are often expressed as hierarchic sequence models, but the hierarchies in these models are typically fairly shallow, reflecting the tendency for memories of superordinate sequence states to become increasingly conflated. This article describes a broad-coverage probabilistic sentence processing model that uses a variant of a left-corner parsing strategy to flatten sentence processing operations in parsing into a similarly shallow hierarchy of learned sequences. The main result of this article is that a broad-coverage model with constraints on hierarchy depth can process large newspaper corpora with the same accuracy as a state-of-the-art parser not defined in terms of sequential working memory operations.
authors.affiliation: The Department of Linguistics The Ohio State University, Columbus, OH 43210-1298, USA. vanschm@ling.ohio-state.edu
authors.forename: Marten
authors.initials: M
authors.lastname: van Schijndel
authors.forename: Andy
authors.initials: A
authors.lastname: Exley
authors.forename: William
authors.initials: W
authors.lastname: Schuler
mesh_terms: D000465:Algorithms; D003198:Computer Simulation / Q000592:standards*; D006801:Humans; D007802:Language; D015233:Models, Statistical; D008962:Models, Theoretical; D015203:Reproducibility of Results
keywords: Computational linguistics; Memory models; Parsing; Sequence models; Working memory