Semantic role labeling is a feasible proposal to shallow semantic parsing. A maximum entropy classifier is used in the semantic role labeling system, which takes syntactic constituents as the labeled units. Some useful features and their combinations are used in the classifier. In the post-processing step, only the roles with the highest probability among the embedding ones are kept. After predicting all the arguments, which have matched the constituents in full parsing trees, a simple rule-based post-processing is applied to correct the arguments that have not matched the constituents in these trees. F1=75.49% and F1=75.60% results are obtained on the development and test set respectively. So far as it is known, this is the best result based on single syntactic parser in literatures. Finally, some proposals for soving the difficulties in semantic role labeling and the future works are given.