Generative Constituent Parsing and Discriminative Dependency Reranking: Experiments on English and French

International audience; We present an architecture for parsing in two steps. A phrase-structure parser builds for each sentence an n-best list of analyses which are converted to dependency trees. These de- pendency structures are then rescored by a dis- criminative reranker. Our method is language agnostic and enables the incorporation of ad- ditional information which are useful for the choice of the best parse candidate. We test our approach on the the Penn Treebank and the French Treebank. Evaluation shows a sig- nificative improvement on different parse met- rics.