Chinese Semantic Role Labeling Basing on Combination Strategy
At present,Chinese semantic role labeling(SRL) systems based on machine learning can be categorized to full parsing based SRL and shallow parsing based SRL,generally.Considering that these two systems have advantage and weakness on performance and robustness respectively,this paper attempts to combine the outputs of these two systems,uses some global features to train combination model for filtering candidate arguments,then resolves roles violating domain knowledge constraints to obtain the final solution to improve performance of labeling.We report the experiments on Chinese PropBank(CPB) 1.0.the F-Score of our combination system reaches 78.41%,Significantly improves performance on the basis of the F-Score(67.34%) of full parsing based system and the F-Score(71.67%) of shallow parsing based system.it is proved that our combination method is effective.