Integrating syntactic priming into an incremental probabilistic parser, with an application to psycholinguistic modeling

Abstract

The psycholinguistic literature provides evidence for syntactic priming, i.e., the tendency to repeat structures. This paper describes a method for incorporating priming into an incremental probabilistic parser. Three models are compared, which involve priming of rules between sentences, within sentences, and within coordinate structures. These models simulate the reading time advantage for parallel structures found in human data, and also yield a small increase in overall parsing accuracy. © 2006 Association for Computational Linguistics.