Two-Stage Graph Convolutional Networks for Relation Extraction

The purpose of relation extraction is to extract semantic relationships between entities in sentences, which can be seen as a classification task. In recent years, the use of graph neural networks to handle relation extraction tasks has become increasingly popular. However, most existing graph-based methods have the following problems: 1) they cannot fully utilize dependency relation information; 2) there is no consistent criterion for pruning dependency trees. To address these issues, we propose a two-stage graph convolutional networks for relation extraction. In the first stage of the model, the node representation, dependency relation type representation and dependency type weight jointly generate new node representations, fully utilizing the dependency relation information. In the second stage, with the help of the adjacency matrix derived from the dependency tree, the graph convolution operation is performed. In this way, the model can automatically complete the pruning operation. We evaluated our proposed method on two public datasets, and the results show that our model outperforms previous studies in terms of F1 score and achieves the best performance. Further ablation experiments also confirm the effectiveness of each component in our proposed model.