Parsing syntactic and semantic dependencies for multiple languages with a pipeline approach

This paper describes a pipelined approach for CoNLL-09 shared task on joint learning of syntactic and semantic dependencies. In the system, we handle syntactic dependency parsing with a transition-based approach and utilize MaltParser as the base model. For SRL, we utilize a Maximum Entropy model to identify predicate senses and classify arguments. Experimental results show that the average performance of our system for all languages achieves 67.81% of macro F1 Score, 78.01% of syntactic accuracy, 56.69% of semantic labeled F1, 71.66% of macro precision and 64.66% of micro recall.