Dependency-based convolutional neural network for drug-drug interaction extraction

Abstract

Drug-drug interactions (DDIs) are crucial for healthcare. Besides DDIs reported in medical knowledge bases such as DrugBank, a large number of latest DDI findings are also reported in unstructured biomedical literature. Extracting DDIs from unstructured biomedical literature is a worthy addition to the existing knowledge bases. Currently, convolutional neural network (CNN) is a state-of-the-art method for DDI extraction. One limitation of CNN is that it neglects long distance dependencies between words in candidate DDI instances, which may be helpful for DDI extraction. In order to incorporate the long distance dependencies between words in candidate DDI instances, in this work, we propose a dependency-based convolutional neural network (DCNN) for DDI extraction. Experiments conducted on the DDIExtraction 2013 corpus show that DCNN using a public state-of-the-art dependency parser achieves an F-score of 70.19%, outperforming CNN by 0.44%. By analyzing errors of DCNN, we find that errors from dependency parsers are propagated into DCNN and affect the performance of DCNN. To reduce error propagation, we design a simple rule to combine CNN with DCNN, that is, using DCNN to extract DDIs in short sentences and CNN to extract DDIs in long distances as most dependency parsers work well for short sentences but bad for long sentences. Finally, our system that combines CNN and DCNN achieves an F-score of 70.81%, outperforming CNN by 1.06% and DNN by 0.62% on the DDIExtraction 2013 corpus.