Recurrent neural network models for disease name recognition using domain invariant features
Table 3: Performance of various models using 25 dimensional CE features, A:Disease name recognition, B: Disease classification task
['[BOLD] Task', '[BOLD] Model', '[BOLD] Validation Set  [BOLD] Precision', '[BOLD] Validation Set  [BOLD] Recall', '[BOLD] Validation Set  [BOLD] F1 Score', '[BOLD] Test Set  [BOLD] Precision', '[BOLD] Test Set  [BOLD] Recall', '[BOLD] Test Set  [BOLD] F1 Score']
For the task A (Disease name recognition) Bi-LSTM and NN models gave competitive performance on the test set, while Bi-RNN and Bi-GRU did not perform so well. On the other hand for the task B, there is 2.08%−3.8% improved performance (F1-score) shown by RNN models over the NN model again on the test set. Bi-LSTM model obtained F1-score of 59.78% while NN model gave 57.56%. As discussed earlier, task B is difficult than task A as disease category is more likely to be influenced by the words falling outside the context window considered in window based methods. This could be reason for RNN models to perform well over the NN model. This hypothesis will be stronger if we observe similar pattern in our other experiments.