Generative re-ranking model for dependency parsing of Italian sentences

We present a general framework for dependency parsing of Italian sentences based on a combination of discriminative and generative models. We use a state-of-the-art discriminative model to obtain a k-best list of candidate structures for the test sentences, and use the generative model to compute the probability of each candidate, and select the most probable one. We present the details of the specific generative model we have employed for the EVALITA'09 task. Results show that by using the generative model we gain around 1% in labeled accuracy (around 7% error reduction) over the discriminative model.