Is artificial data useful for biomedical Natural Language Processing algorithms?
Table 7: Temporal relations extraction for OVERLAP for CNN and Naive Bayes (NB), test-temp. Only the real/generated text between events serves as input. Best performing models for data augmentation experiments are highlighted in bold. We report results for the models trained using: real all training data from the i2b2 task augmented with the generated gen data, real all data only, 2 × real all data upsampled twice, real reduced data only, gen data only.
['[EMPTY]', 'BiLSTM']
They are similar to the ones performed for the phenotyping dataset. Note that we reduce the initial training set provided by the task due to particularities of our generation procedure. In our data augmentation experiments we add this reduced generated data to all the provided real training data (real all).