We adopt graph convolutional networks (GCN) (Kipf and Welling, 2017; Marcheggiani and Titov, 2017) to encode graph structures over the input data, applying graph convolution operations to generate entity and word representations in a latent space.
to other encoders such a Tree-LSTM (Tai et al., 2015), GCN can cover more complete contextual information from dependency parses because, for each word, it captures all parse tree neighbors of the word, rather than just the child nodes of the word.