Improving Distant Supervised Relation Extraction by Dynamic Neural Network
Table 3: Hyper-parameter settings used in our model.
['[BOLD] Parameter Name', '[BOLD] Value']
We use the same hyper-parameter settings in PCNN Zeng et al. The dimension of entity type and relation tuple element embedding are both set to 50. GCN layers are set to 2. The cross-entropy loss function is applied to train our model. The Adadelta optimizer Zeiler Moreover, the dropout strategy Srivastava et al. We implement the network based on PyTorch.