Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition
Table 2: Performance comparison of the proposed model CWBLSTM with baseline models on test set of different datasets. Here Accuracy represents token level accuracy in tagging.
['[BOLD] Tasks', '[BOLD] Models', '[BOLD] Accuracy', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1 Score']
We can observe that it outperforms all three baselines in each of the three tasks. In particular, when comparing with CharCNN, differences are significant for Drug NER and Disease NER tasks but difference is insignificant for Clinical NER. The proposed model improved the recall by 5% to gain about 2.5% of relative improvement in F1 score over the second best method CharCNN for the Disease NER task. For the Drug NER task, relative improvement of more than 3% is observed for all three measures, precision, recall and F1 score over the CharCNN model. The relatively weaker performance on Clinical NER task could be attributed to use of many non standard acronyms and abbreviations which makes it difficult for character based embedding models to learn appropriate representation.