Abstract
This chapter presents a discriminative modeling technique which corrects the errors made by an automatic parser. The model is similar to reranking; however, it does not require the generation of k-best lists as in MCDonald et al. (2005), McDonald and Pereira (2006), Charniak and Johnson (2005), and Hall (2007).
The corrective strategy employed by our technique is to explore a set of candidate parses which are constructed by making structurally– local perturbations to an automatically generated parse tree.