# Reinforcement Learning-Based Intent Classification of Chinese Questions About Respiratory Diseases
## 4 Experiments
### Classification Results
\begin{table}
\begin{tabular}{l l l} \hline
**Model** & **Not Used Keywords** & **Used Keywords** \\ \hline Mean vector & 81.26 & 87.36 \\ CNN & 85.86 & 87.24 \\ LSTM & 86.90 & 89.77 \\ S-LSTM & 87.59 & 89.31 \\ Bi-LSTM & 89.43 * & 91.03 * \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the results after adding keyword information and the results of unused keyword information (Accuracy * 100). The asterisk (*) indicates the best result. \begin{table}
\begin{tabular}{l l l l} \hline
**Model** & **CORD\_28000** & **CORD\_8000** & **CMID** \\ \hline Mean vector & 82.96 & 81.26 & 75.3 \\ BERT & 94.67 & 86.32 & 81.65 \\ MC-BERT & 94.47 & 88.67 & 83.15 \\ BERT-ehealth & 94.82 & 89.14 & 82.85 \\ ERNIE-Bot & 90.33 & 89.12 & 83.15 \\ GCN\_Tf-Idf & 88.94 & 84.37 & 81.35 \\ GCN & 91.76 & 88.12 & 82.55 \\ LSTM & 94.15 & 86.9 & 81.9 \\ Bi-LSTM & 94.28 & 89.43 & 82.9 \\ Bi-LSTM + attention & 94.44 & 90.11 & 83.4 \\
**KD-GCN** & 94.88 & 90.13 & 83.65 \\
**KD-LSTM\_weight** & 95.0 * & 90.92 * & 84.45 * \\
**KD-LSTM\_vec** & 94.94 & 91.84 & 84.8 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of results from different models (Accuracy * 100). The bolded and highlighted models in the table are the ones we proposed. The asterisk (*) indicates the best result. the amount of data is relatively small. As the amount of data increases, the result of the KD-LSTM_weight model exceeds that of the KD-LSTM_vec model. Although KD-GCN did not achieve the best results, it showed the highest improvement compared to the baseline model. On the contrary, using tf-idf in GCN_Tf-Idf not only did not improve the results but also significantly decreased their value. In addition, the BERT model does not work well on these two datasets. In addition, MC-BERT and BERT-ehealth used medical and biological corpora during training, but the results did not significant improvement. Because the training corpus was not used, ERNIE-Bot achieved relatively stable classification results on the CQRD_28000 and CQRD_8000 datasets. To verify the robustness of the model, a five-fold cross-validation method was employed; the primary advantage of K-fold cross-validation is that it utilizes all data points in the dataset for both training and testing, thereby enhancing the reliability of the results. As shown in Table 3, when comparing different classification methods using cross-validation, the KD-LSTM_weight model achieved the highest accuracy on the first and fifth test sets. Meanwhile, the KD-LSTM_vec model performed best on the third and fourth test sets. Despite achieving the best results in the second test set, the performance of the KD-GCN model was unsatisfactory across other test sets. From Table 4, we evaluate the accuracy of specific categories on the CQRD_28000 dataset. Experimental results demonstrate that the KD-LSTM and KD-GCN models exhibit excellent classification performance for minority classes, effectively addressing the issue of data imbalance to some extent. ERNIE-Bot exhibits remarkable performance in the PRE category. Upon analyzing the characteristics of the PRE category, it was discovered that it encompasses a substantial amount of vocabulary pertaining to food, beverages, and sports types, among others. The ERNIE-Bot demonstrates a high capability in recognizing these attributes. To evaluate the statistical significance of the classification model's performance on minority classes, we employed the Paired t-Test for significance testing. The baseline model used for comparison was Bi-LSTM, and each of the other models were sequentially tested against it for significance. The results indicate that KD-LSTM_weight demonstrates statistically significant improvements. The keywords for each CQRD in the CQRD_8000 dataset were manually summarized. In the reinforcement learning-based intention classification model, keywords are initially identified, and the quality of these keywords directly influences the quality of the subsequent results.