Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
TABLE VIII: Comparative Results (F{}_{1}-score) of Models With Different Block Number n_{r} and Dilation Factor d=d_{b}^{i-1}
['Block Number', 'Dilation Factor d_{b} = 1', 'Dilation Factor d_{b} = 2', 'Dilation Factor d_{b} = 3']
Once the window size is determined, there are two main approaches to capture more contextual information. The one is to make the network deeper by increasing the block number n_{r}. The other one is to expand the receptive field by increasing the dilation factor d. To investigate the impact of the block number n_{r} and dilation factor d=d_{b}^{i-1}, we compare the performance of models with different parameter values. Firstly, when n_{r}=2 and d_{b}=3, the model achieves the best performance with a F{}_{1}-score of 91.32\%. This confirms the benefit of expanding the receptive field. Secondly, when n_{r}=3 and d_{b}=1, the F{}_{1}-score is 91.30\%, which is only slightly lower than 91.32\%. It indicates that making the network deeper has a same effect as well as expanding the receptive field. Thirdly, when n_{r}=4, performance of the model gradually deteriorates as the increase of d_{b}. We can obtain same observation when d_{b}=3 and n_{r} gradually increases. The above observations show that long-distance contextual information is not necessary to the Chinese CNER task.