This paper describes our contribution to the semantic role labeling task (SRL-only) of the CoNLL-2009 shared task in the closed chal- lenge (Hajiˇ c et al., 2009). Our system con- sists of a pipeline of independent, local clas- sifiers that identify the predicate sense, the ar- guments of the predicates, and the argument labels. Using these local models, we carried out a beam search to generate a pool of candi- dates. We then reranked the candidates using a joint learning approach that combines the lo- cal models and proposition features. To address the multilingual nature of the data, we implemented a feature selection procedure that systematically explored the feature space, yielding significant gains over a standard set of features. Our system achieved the second best semantic score overall with an average la- beled semantic F1 of 80.31. It obtained the best F1 score on the Chinese and German data and the second best one on English.
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