Broad-coverage parsing using human-like memory constraints

  • Authors:
  • William Schuler;Samir AbdelRahman;Tim Miller;Lane Schwartz

  • Affiliations:
  • -;-;-;-

  • Venue:
  • Computational Linguistics
  • Year:
  • 2010

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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.