DREAM: Drug-drug interaction extraction with enhanced dependency graph and attention mechanism

Abstract

Drug-drug interactions (DDIs) aim at describing the effect relations produced by a combination of two or more drugs. It is an important semantic processing task in the field of bioinformatics such as pharmacovigilance and clinical research. Recently, graph neural networks are applied on dependency graph to promote the performance of DDI extraction with better semantic representations. However, current method concentrates more on first-order dependency relations and cannot discriminate the connected nodes properly. To better incorporate the dependency relations and improve the representations, we propose a novel DDI extraction method named Drug-drug Interactions extRaction with Enhanced Dependency Graph and Attention Mechanism in this work. Specifically, the dependency graph is enhanced with some potential long-range words to complete the semantic information and fit the aggregation process of graph neural networks. And graph attention mechanism is adopted to further improve word representation by discriminating the connected nodes according to the specific task. Numerical experiments on DDIExtraction 2013 corpus, the benchmark corpus for this domain, demonstrate the superiority of our proposed method.

Keywords: Dependency relations; Drug-drug interactions; Enhanced dependency graph; Graph attention networks; Relation extraction.