Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
TABLE V: Comparative Results between Ensemble Models and Our RD-CNN-CRF Model
['Methods', 'Precision', 'Recall', 'F{}_{1}-score']
From the table, we observe that our proposed RD-CNN-CRF model achieves the best results among all the models. Li et al. [Li2017Recurrent] gets the worst performance because of two reasons. One is that their word-level approach inevitably has wrong segmentation, which leads to boundary errors when recognition. The other one is that since clinical texts are labeled in word level, there exists much more words than characters, so the corpus may contain many rare words which are difficult to be recognized. In fact, except for Li et al. [Li2017Recurrent], the other methods all regarded the CNER task as a sequence labeling problem in character level. It shows the benefits of the character-level CNER for Chinese.