Semantic Role Classification Based on Peking University Chinese NetBank

Among all the researches on semantic role labeling(SRL),one important method which has been carried out by many researchers is to convert the task into a classification problem by selecting features,and thenapplying different kinds of classifiers.While almost all the researches based on this kind of supervised learning have been done on the same corpus-Penn Proposition Bank,here we test the same method on a new corpus—Peking University Chinese NetBank,with the goal to figure out whether the wildly used features have a strong dependence on corpus.The experiments have shown that the method and the features have good performance on the new corpus.And compared to the PropBank,some features play crucial roles in classification on the new corpus.