We demonstrate how incorporating knowledge directly from the FrameNet graph structure improves the performance of a neural network-based frame identification system.
we construct a bidirectional LSTM with a loss function that incorporates various graph-and corpus-based frame embeddings for learning and ultimately achieves strong performance gains with the graphbased embeddings over corpus-based embeddings alone.