Document-Level Biomedical Relation Extraction via Knowledge-Enhanced Graph and Dynamic Generative Adversarial Networks

Biomedical document-level relation extraction (RE) aims to extract relation facts from unstructured biomedical documents and plays an important role in downstream tasks. Graph-based methods solve the problem that sequence-based methods cannot extract long-distance entity relationships, but ignore the fact that graph node connections should be dynamic rather than static. Besides, the existing methods usually introduce external knowledge to address the method's performance bottleneck caused by the limited information contained in the dataset itself. But they fail to consider utilizing external knowledge through explicitly enriching graph connectivity. For the above problems, we propose a novel document-level relation extraction model based on a knowledge-enhanced graph and dynamic generative adversarial network (KG-DGAN). Specifically, a knowledge-enhanced graph is constructed based on the documents and external knowledge information together, where the external knowledge is used to explicitly enhance the connectivity of the graph. Then, the dynamic generative adversarial network (DGAN) can dynamically soften the edge weights and node representations, which reduces the redundant information and enhances useful information during aggregation. We evaluate our method on the widely used CDR and CHR dataset. The final experimental results confirm that the proposed method achieves novel state-of-the-art performances.