A Probabilistic Model of Lexical and Access and Disambiguation

The problems of access-retrieving linguistic structure from some mental grammor -and disomblguatlon-choosing among these structures to correctly parse ambiguous linguistic input-are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disombiguation, and the processing of garden-path sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disombiguation at the lexical, idiomatic, and syntactic levels. For example, psycholinguistic theories of lexical access and idiom access and parsing theories of syntactic rule access hove almost no commonality in methodology or coverage of psycholinguistic data. This article presents o single probabilistic algorithm which models both the access and disambiguation of linguistic knowledge. The algorithm is based on a parallel porser which ranks constructions for access, and interpretations for disambiguation, by their conditional probability. Low-ranked constructions and interpretations ore pruned through beam-search; this pruning accounts, among other things, for the garden-path effect. I show that this motivated probabilistic treatment accounts for a wide variety of psycholinguistic results, arguing for a more uniform representation of linguistic knowledge and for the use of probabilistically-enriched grammars and interpreters as models of human knowledge of ond processing of language.