The current PASSAGE syntactic representation is the result of 9 years of constant evolution with the aim of providing a common ground for evaluating parsers of French whatever their type and supporting theory. In this paper we present the latest developments concerning the formalism and show first through a review of basic linguistic phenomena that it is a plausible minimal common ground for representing French syntax in the context of generic black box quantitative objective evaluation. For the phenomena reviewed, which include: the notion of syntactic head, apposition, control and coordination, we explain how PASSAGE representation relates to other syntactic representation schemes for French and English, slightly extending the annotation to address English when needed. Second, we describe the XML format chosen for PASSAGE and show that it is compliant with the latest propositions in terms of linguistic annotation standard. We conclude discussing the influence that corpus-based evaluation has on the characteristics of syntactic representation when willing to assess the performance of any kind of parser.

Abstract. DeSR is a statistical transition-based dependency parser which learns from annotated corpora which actions to perform for building parse trees while scanning a sentence. We describe recent improvements to the parser, in particular stacked parsing, exploiting a beam search strategy and using a Multilayer Perceptron classifier. For the Evalita 2009 Dependency Parsing task DesR was configured to use a combination of stacked parsers. The stacked combination achieved the best accuracy scores in both the main and pilot subtasks. The contribution to the result of various choices is analyzed, in particular for taking advantage of the peculiar features of the TUT Treebank.

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We study the use of Confidence Measures (CM) for erroneous constituent discrimination in an Interactive Predictive Parsing (IPP) framework. The IPP framework allows to build interactive tree annotation systems that can help human correctors in constructing error-free parse trees with little effort (compared to manually postediting the trees obtained from an automatic parser). We show that CMs can help in detecting erroneous constituents more quickly through all the IPP process. We present two methods for precalculating the confidence threshold (globally and per-interaction), and observe that CMs remain highly discriminant as the IPP process advances.