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This paper explores the contributions of various features in semantic role labeling. Moreover, an optimal set of features is selected using a greedy strategy. Finally, an effective headword-driven pruning algorithm is proposed to filter out irrelavant instances. Evaluation on the CoNLL'2005 SRL benchmark corpus shows that our method achieved comparable performance with the best-reported ones on a single automatic parse tree.