  The class imbalance problem can cause machine learning models to produce an
undesirable performance on the minority class as well as the whole dataset.
Using data augmentation techniques to increase the number of samples is one way
to tackle this problem. We introduce a novel counterfactual data augmentation
by verb replacement for the identification of medical claims. In addition, we
investigate the impact of this method and compare it with 3 other data
augmentation techniques, showing that the proposed method can result in a
significant (relative) improvement in the minority class.
