Leveraging Weighted Dependencies in Graph Convolutional Networks for Biomedical Relation Detection

The goal of biomedical relation extraction is to identify and extract high-quality information from extensive medical texts, which is crucial for advancing biomedical research. Recent advancements have led to significant progress in this field, with various methods demonstrating impressive results. Nevertheless, there remains substantial potential for further enhancement. In this paper, we introduce a Weighted Graph Convolutional Neural Network (WGCN) model designed specifically for extracting biomedical relations from medical texts. Our approach begins with obtaining the syntactic dependencies of the given sentences. We then integrate these dependencies with weighted information to improve relational extraction accuracy. Specifically, our method involves constructing an adjacency matrix and a dependency matrix based on the relations between words in the text, followed by the development of a corresponding weight matrix to account for the significance of different dependencies. This allows each dependency to contribute differently to the extraction of relation triples. Moreover, to address potential noise introduced by numerous dependencies within sentences, we implement a pruning strategy for the dependency tree. Experimental results on three publicly available biomedical datasets demonstrate that our proposed method is both effective and superior to the best-performing existing models. Further ablation studies confirm the effectiveness of each component within our model.