Exposure-based models of human parsing: Evidence for the use of coarse-grained (nonlexical) statistical records

Several current models of human parsing maintain that initial structural decisions are 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 previous 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 cooccurrence 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 parsers must draw upon records or representations that capture statistical regularities beyond the lexical level. This poses problems for several parsing models in the literature.