We present an end-to-end method for learning verb-specific semantic frames with feedforward neural network (FNN). Previous works in this area mainly adopt a multi-step procedure including part-of-speech tagging, dependency parsing and so on. On the contrary, our method uses a FNN model that maps verbspecific sentences directly to semantic frames. The simple model gets good results on annotated data and has a good generalization ability. Finally we get 0.82 F-score on 63 verbs and 0.73 F-score on 407 verbs.

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