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.Aspects of this work were supported by ESRC grant No. R0023 4062 to Don Mitchell, by a Spanish Government grant DGICVT No. PB-92-0656-C04-02 to Fernando Cuetos, and by the Belgian National Fonds voor Wetenschappelijk Onderzoek, of which Marc Brysbaert is a Research Associate. We are grateful to Chuck Clifton, Barbara Hemforth, Martin Pickering, Matt Traxler, and an anonymous reviewer-all of whom made helpful comments on an earlier draft of the paper.