Corrective Dependency Parsing

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. We train a model which makes local, corrective decisions in order to optimize for parsing performance. The technique is independent of the parser generating the first set of parses. We show in this chapter that the only requirement for this technique is the ability to define a local neighborhood in which a large number of the errors occur.

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