Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
TABLE IV: Comparative Results between Basic Bi-LSTM-CRF and Our RD-CNN-CRF Model
['Methods', 'Precision', 'Recall', 'F{}_{1}-score']
Compared with Basic Bi-LSTM-CRF Since Gridach [Gridach2017Character], Habibi et al. [Habibi2017Deep] and Zeng et al. [Zeng2017LSTM] have successfully employed Bi-LSTM-CRF models with no additional features for the English CNER. In this section, we first compare the proposed RD-CNN-CRF model with the Bi-LSTM-CRF models. As our previous work pointed that characters are better than words [Xia2017Clinical]. In this experiment, we adapt Bi-LSTM-CRF for the Chinese texts by using the Chinese characters instead of words as inputs. We implement two models with and without dictionary features. The hidden vector size of basic Bi-LSTM-CRF model is 256 which has the best performance through our test.