## 4 Performance Analysis
### Results and Discussion
#### 4.3.2 RC Task Evaluation  
To evaluate the effectiveness of MDA in the RC task, two datasets are selected to construct the experiments. In the experiments, the MDA and the EDA are used to generate the augmented data, and then the augmented data are combined with the original data to generate the synthetic data. In addition, three deep learning models that are selected to extract triplets for the RC task are the multi-head attention model [48], the ETL-Span model [49], and the CasRel model [50]. First, the multi-head attention model [48] can simultaneously train the entity extraction module and relation extraction module to recognize multiple relations for each entity. Second, the ETL-Span model [49] is designed as a novel tag schema that can transform the extraction task into tagging task. Third, the CasRel model [50] utilizes a cascade framework that can alleviate the problem of overlapping triplets. Finally, the performance of these models on original data and the synthetic data are evaluated.  
For the deep learning models, the RoBERTa model is exploited to capture the features in sentences, the pre-trained weights are the same as the weights of the official release. In addition, the batch size is set to 8, the learning rate is set to 1E-5, and the maximum length of the input sentence is set to 128. Moreover, the stopping mechanism that can stop the training process is also adopted in the experiments.  
The experimental process of the RC task is the same as the process of the NER task. Specifically, several samples are selected from original data, and these samples are adopted to generate augmented data on the EDA and the MDA with a 40% replacement probability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**CCKS2020**} & \multicolumn{3}{c}{**BITEmrNER**} \\ \cline{2-7}  & **Original** & **EDA** & **MDA** & **Original** & **EDA** & **MDA** \\ \hline BERT + CRF & 80.56 & 81.34 & 83.29 & 82.67 & 83.55 & 85.51 \\ BiLSTM + CRF & 81.00 & 82.04 & 83.45 & 83.35 & 84.22 & 85.88 \\ Ra-RC & 82.87 & 83.69 & 85.64 & 84.29 & 84.69 & 86.64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: F1-score of the NER task on the CCKS2020 dataset and the BITEmrNER dataset.  
After that, the augmented data are fused with original data to generate synthetic data for experiments. For the CHIP2020 dataset, the MDA optimizes the performance of these models compared to the EDA, and it achieves 2.11% improvements in F1-score over the original data. As can be seen from Table 6, the EDA does not improve the performance of the models in the RC task, because traditional text data augmentation abandons the semantic information that is important to extract the triplets in the RC task. However, the methods of the MDA highlight the importance of label data in the original data, and the method of capturing keywords is exploited to stabilize the semantics of sentences. In addition, the BITEmrRC dataset consists of many electronic medical records, and it contains complex technical terms and overlapping triplets. Hence, the results of the F1-score in Table 6 show that the MDA has excellent performance. Especially, the RoBERTa + CasRel model can alleviate the problems of the complex medical knowledge recognition and overlapping triplets with the data augmentation of the MDA.