EVALITA 2007, the first edition of the initiative devoted to the evaluation of Natural Language Processing tools for Italian, provided a shared framework where participants ’ systems had the possibility to be evaluated on five different tasks, namely Part of Speech

Abstract. This paper describes our participation in the EVALITA 2009 Dependency Parsing Task with a version of MaltParser. Reusing feature models developed in the CoNLL shared task 2007, we evaluated four different parsing algorithms implemented in MaltParser and found that the best results were achieved with Covington’s non-projective parsing algorithm. In the final evaluation, our system finished third in the main task and second in the pilot task. Keywords: Dependency parsing, Italian. 1

Abstract. In this paper we describe our participation at the EVALITA 2009 Constituency Parsing Task. We used the Berkeley Parser, obtaining the best F1, that is 78.73. This result corresponds to an increment of 15.85 % with respect to the best result obtained at EVALITA 2007 by the Bikel’s parser (F1 = 67.96). A further important advantage of the Berkeley parser is that it does not require any language adaptation in addition to the need of retraining it on the new treebank. For comparison, we also report the results obtained by the Bikel’s parser on the 2009 treebank. Keywords: Constituency parsing, statistical parsing, Italian. 1

The following are the guidelines for the parsing task of the EVALITA 2009 evaluation campaign. The task includes two tracks, i.e. Dependency Parsing and Constituency Parsing. The participation is open to parsing systems pursuing different approaches, e.g. rule-based vs

Abstract. The aim of Evalita Parsing Task is at defining and extending the state of the art for parsing Italian by encouraging the application of existing models and approaches. Therefore, as in the first edition, the Task includes two tracks, i.e. dependency and constituency. This second track is based on a development set in a format, which is an adaptation for Italian of the Penn Treebank format, and has been applied by conversion to an existing dependency Italian treebank. The paper describes the constituency track and the data for development and testing of the participant systems. Moreover it presents and discusses the results, which positively compare with those obtained for constituency parsing in the Evalita’07 Parsing Task.