But the CCG account is a competence model as well as a performance model, in that it seeks to unify category representations used in processing with learned generalizations about argument structure; whereas the model described in this paper is exclusively a performance model, allowing generalizations about lexical argument structures to be learned in some other representation, then combined with probabilistic information about parsing strategies to yield a set of derived incomplete constituents.
the remainder of this paper is organized as follows: Section 2 describes related approaches to parsing with stack bounds; Section 3 describes an existing bounded-stack parsing framework using a rightcorner transform defined over individual trees; Section 4 describes a redefinition of this transform to ap-ply to entire probabilistic grammars, cast as infinite sets of generable trees; and Section 5 describes an evaluation of this transform on the Wall Street Journal corpus of the Penn treebank showing improved results for a transformed bounded-stack version of a probabilistic grammar over the original unbounded grammar.