Drug-drug interaction relation extraction with deep convolutional neural networks
Ika Novita Dewi, Shoubin Dong, Jinlong Hu · 2017
Drug-Drug Interaction (DDI) relation extraction is a multi-class classification problem that aims to predict the interaction between drugs in a sentence. The configuration of Convolutional Neural Network (CNN) in relation extraction usually applied shallow architecture layers, which may make the information in given input text is not fully captured, thus fail to capture a long sentence containing the detected drug relation or some irrelevant word captured during the feature extraction process. This paper proposed an extending depth of the CNN layer called DeepCNN for DDI relation extraction. The DeepCNN learns the high quality of the learning representation so that it is able to cover long input sentences as the typical of DDIExtraction dataset. We use multi-channel word-embedding to enlarge the vocabulary and decrease the number of unknown words, and Adam update rule to automatically learn the network parameters of DeepCNN for DDI relation extraction. The experiments show that the architecture of 10 layers DeepCNN successfully obtained the significant improvement compared to the previous CNN method in DDI relation extraction. The result proves that CNN is a robust and well-deserved for DDI relation extraction.