CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction - Zeng, Daojian; Zhao, Chao; Quan, Zhe (Frontiers in Genetics, 2021)
Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development. In this task, some relational facts can only be inferred from the documents rather than single sentences. Recently, researchers investigate graph-based approaches to extract relations across sentences. It iteratively combines the information from neighbor nodes to model the interactions in entity mentions that exist in different sentences. Despite their success, one severe limitation of the graph-based approaches is the over-smoothing problem, which decreases the model distinguishing ability. In this paper, we propose CID-GCN, an effective Graph Convolutional Network (GCN) with a gating mechanism, for CID relation extraction. Specifically, we construct a heterogeneous graph that contains mention, sentence, and entity nodes. To address the over-smoothing problem, we subsequently exploit graph convolution operation with a gating mechanism to aggregate interactive information on the constructed graph. The experimental results demonstrate that our approach significantly outperforms the baselines.