## 4 Performance Analysis
### Results and Discussion
#### 4.3.1 NER Task Evaluation  
In order to evaluate the superiority of the MDA for the NER task, we choose two datasets to repeat the experiment five times. In the experiment, different data augmentation methods are exploited to generate the augmented data, then the original data is combined with the augmented data to build the synthetic data. Furthermore, three baseline models are utilized to recognize different entities. First, the model of BiLSTM-CRF is proposed by [PERSON] et al. (2018), where BiLSTM-CRF is proved to be a better NER model than other options such as BiLSTM, LSTM, CRF alone, or combinations such as LSTM-CRF. Second, BERT-CRF is utilized for the baseline in Chinese NER tasks by many researchers ([PERSON] et al., 2018) because BERT is a widely used embedding model in many NLP tasks. Third, the Ra-RC model ([PERSON] et al., 2018) combines radical features and a deep learning structure, and the performance of these models is evaluated based on the original data and the synthetic data. \begin{table} \begin{tabular}{c c c} \hline \hline
**Datasets** & **Text** & **Label** \\ \hline \multirow{6}{*}{CCKS2019} & \multirow{6}{*}{(Before the end of 4 months, the patient was diagnosed as ‘gastric anturn cancer’ in our hospital due to ‘lower quadrant abdominal pain with acid regurgitation and ‘belching’. The patient underwent ‘distal gastrectomy’ under general anesthesia. SOX (Oxaliplatin+ Tegafur) regimen chemotherapy was performed in our hospital.)} & \multirow{6}{*}{(‘abdominal’—’anatomic site’, ‘gastric anturn cancer’—’diseases and diagnoses’, ‘lower quadrant abdominal pain with acid regurgitation and ‘lower quadrant abdominal pain with acid regurgitation and ‘sporation’, ‘Oxaliplatin’–’drug’, ‘Tegafur’—drug’, ‘} \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \hline \hline \end{tabular}  (Chilp2020)  
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For the deep learning models, the BERT model is employed to capture the features in sentences, and the CRF model is exploited to mark the recognized entities. In addition, the pre-trained weights that are obtained from the official release are not modified. Therefore, in this study, the batch size is set to 16, the learning rate is set to 1E-5, and the maximum length of the input sentence is set to 128. Table 5 shows the results of the F1-score on the CCKS2020 dataset and the BITEmrNER dataset. Specifically, EDA and MDA are used to generate synthetic data from the original data, and the number of synthetic data is equal to the number of the pre-set. For the EDA settings, the four transitions (SR, RI, RS, RD) are randomly selected with a 40% replacement probability. For MDA Settings, the four methods (MKR, WI, WS, WD) are successively utilized to expand with a replacement probability of 40%. For the CCKS2019 dataset, it can be observed from Table 5 that the EDA outperforms the original data in the entity recognition by 1.04% and the MDA outperforms the original data by 2.77% in the best performance. The improvement of the EDA can be explained by changing the context in four transitions, which causes the model to lose the context information of the entity.