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Computational Linguistics

Paola Merlo, Editor
March 2010, Vol. 36, No. 1, Pages 1-30
(doi: 10.1162/coli.2010.36.1.36100)
© 2010 Association for Computational Linguistics
Broad-Coverage Parsing Using Human-Like Memory Constraints
Article PDF (499.19 KB)
Abstract

Human syntactic processing shows many signs of taking place within a general-purpose short-term memory. But this kind of memory is known to have a severely constrained storage capacity—possibly constrained to as few as three or four distinct elements. This article describes a model of syntactic processing that operates successfully within these severe constraints, by recognizing constituents in a right-corner transformed representation (a variant of left-corner parsing) and mapping this representation to random variables in a Hierarchic Hidden Markov Model, a factored time-series model which probabilistically models the contents of a bounded memory store over time. Evaluations of the coverage of this model on a large syntactically annotated corpus of English sentences, and the accuracy of a a bounded-memory parsing strategy based on this model, suggest this model may be cognitively plausible.