Recurrent neural network models for disease name recognition using domain invariant features
Table 7: Comparisons of our best model results and state-of-art results. SM-SVM :Soft Margin Support Vector Machine
['[BOLD] Task', '[BOLD] Model', '[BOLD] Validation Set  [BOLD] P', '[BOLD] Validation Set  [BOLD] R', '[BOLD] Validation Set  [BOLD] F', '[BOLD] Test Set  [BOLD] P', '[BOLD] Test Set  [BOLD] R', '[BOLD] Test Set  [BOLD] F']
BANNER is a CRF based bio entity recognition model, which uses general linguistic, orthographic, syntactic dependency features. (F1-score = 81.8) is better than that of our RNN models but it should be noted that competitive result (F1-score = 79.13%) is obtained by the proposed Bi-LSTM model which does not depend on any feature engineering or domain-specific resources and is using only word embedding features trained in unsupervised manner on a huge corpus.