Multilingual Dependency Parsing and Domain Adaptation using DeSR

We describe our experiments using the DeSR parser in the multilingual and do- main adaptation tracks of the CoNLL 2007 shared task. DeSR implements an incre- mental deterministic Shift/Reduce parsing algorithm, using specific rules to handle non-projective dependencies. For the multi- lingual track we adopted a second order averaged perceptron and performed feature selection to tune a feature model for each language. For the domain adaptation track we applied a tree revision method which learns how to correct the mistakes made by the base parser on the adaptation domain.