Convolutional LSTM Network with Hierarchical Attention for Relation Classification in Clinical Texts
Li Tang, Fei Teng, Zheng Ma, Lufei Huang, Ming Bo Xiao, Xuan Li · 2019
Identifying relation from clinical texts is a complex and challenging task due to the specific biomedical knowledge. Existing methods for this work generally have the misclassification problem caused by sample class imbalance. In this paper, we propose a hierarchical attention-based convolutional long short-term memory (ConvLSTM) network model to solve this problem. We construct a sentence as multi-dimensional hierarchical sequence and directly learn local and global context information by a single-layer ConvLSTM network. Besides, a hierarchical attention-based pooling is built to capture the parts of a sentence that are relevant with the target semantic relation. Experiments on the 2010 i2b2/VA relation dataset show that our model outperforms several previous state-of-the-art models without relying on any external features.