Exposure-based models of human parsing: Evidence for the use of course-grained (non-lexical) statistical records.

Several current models of human parsing maintain that initial structural decisions ave influenced (or tuned) by the listener's or reader's prior contact with language. The precise workings of these models depend upon the ''grain,'' or level of detail, at which precious exposures to language are analyzed and used to influence parsing decisions. Some models are premised upon the use of fine-grained records (such as lexical co-occurrence statistics). Others use coarser measures. The present paper considers the viability of models based exclusively on the use of fine-grained lexical records. The results of several studies are reviewed and the evidence suggests that, if they are to account for the data, experience-based papers must draw upon records or representations that capture statistical regularities beyond the lexical level. This poses problems for several parsing models in the literature.