Probabilistic Grammars as Models of Gradience in Language Processing

This article deals with gradience in human sentence processing. We review the experimental evidence for the role of experience in guiding the decisions of the sentence processor. Based on this evidence, we argue that the gradient behavior observed in the processing of certain syntactic constructions can be traced back to the amount of past experience that the processor has had with these constructions. In modeling terms, linguistic experience can be approximated using large, balanced corpora. We give an overview of corpus-based and probabilistic models in the literature that have exploited this fact, and hence are well placed to make gradient predictions about processing behavior. Finally, we discuss a number of questions regarding the relationship between gradience in sentence processing and gradient grammaticality, and come to the conclusion that these two phenomena should be treated separately in conceptual and modeling