SGAT: a Self-supervised Graph Attention Network for Biomedical Relation Extraction
Qiming Liu, Zhihao Yang, Lei Wang, Yin Zhang⋆, Hongfei Lin, Jinzhong Ning · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2021
The goal of relation extraction task is to classify texts containing entity pairs into predefined relation types. Biomedical relation extraction can extract high-quality information from massive medical texts, which plays an important role in biomedical research. In this paper, we propose a self-supervised graph attention network to extract biomedical relations from the complex and noisy biomedical texts. The model incorporates self-supervision within the standard graph attention mechanism. Specifically, the model applies the graph attention mechanism to reduce the influence of noisy words and introduces dependency-based parse trees to construct a self-supervised task. With the supervision of dependency-based parse trees, the graph attention network can not only improve its capacity of learning syntactic information but also alleviate its lack of interpretability. Additionally, we use Gumbel Tree-GRU to obtain sentence information for relation classification. Our model achieves state-of-the-art performance on the DDIExtraction 2013 and ChemProt datasets, respectively, which suggests that our proposed model can effectively improve the performance of biomedical relation extraction.