  Deep learning research on relation classification has achieved solid
performance in the general domain. This study proposes a convolutional neural
network (CNN) architecture with a multi-pooling operation for medical relation
classification on clinical records and explores a loss function with a
category-level constraint matrix. Experiments using the 2010 i2b2/VA relation
corpus demonstrate these models, which do not depend on any external features,
outperform previous single-model methods and our best model is competitive with
the existing ensemble-based method.
