Finally, the hidden states of entity mentions from both LSTMs are taken as inputs to a logistic regression classifier to make a prediction: where h ✏ j is the hidden state of entity ✏ j .
we train our models with a cross-entropy loss over a set of gold standard data: where X i is an input graph, y i is the gold class label of X i , and ✓ is the model parameters.