UPC: Experiments with Joint Learning within SemEval Task 9

This paper describes UPC's participation in the SemEval-2007 task 9 (Marquez et al., 2007). We addressed all four subtasks using supervised learning. The paper introduces several novel issues: (a) for the SRL task, we propose a novel reranking algorithm based on the re-ranking Perceptron of Collins and Duffy (2002); and (b) for the same task we introduce a new set of global features that extract information not only at proposition level but also from the complete set of frame candidates. We show that in the SemEval setting, i.e., small training corpora, this approach outperforms previous work. Additionally, we added NSD and NER information in the global SRL model but this experiment was unsuccessful.