Extracting Drug-drug Interactions with a Dependency-based Graph Convolution Neural Network

Drug-drug interactions (DDIs) play a key role in various applications of biomedicine such as pharmacovigilance. DDIs are frequently reported in biomedical publications, making them an effective source for DDI extraction. Although neural networks have achieved competitive performances in DDI extraction, previous work depended on dependency paths to remove noise from the sentences of biomedical publications. However, such method may ignore crucial information about DDIs. Effectively exploiting much dependency information can improve DDI extraction. In this article, we proposed a model that combines the graph convolution neural network (GCNN) and bidirectional long short-term memory (BiLSTM) to extract DDI interactions from entire dependency graphs of sentences. We evaluated our model in the benchmark corpus for this domain, namely the DDIExtraction 2013 corpus. Our model achieved the state-of-the-art result (77.0% in F1), which are superior to the results reported in previous work. The code is available at https://github.com/woodyXwt/DDI_extraction.