# Reinforcement Learning-Based Intent Classification of Chinese Questions About Respiratory Diseases
## 5 Discussion  
In this paper, we propose a keyword-based reinforcement learning approach to increase CQRD intent classification accuracy. First, we identify the keywords through reinforcement learning. We utilize a word frequency reward function to assign multiple weights to each word to assist in the generation of the reward and keyword category judgment. For keywords that have been assigned categories, we employ KD-LSTM and KD-GCN to construct CQRD representation. In KD-LSTM, we have designed two methods to utilize keyword information: the method based on word weight and the method based on category vector. The method based on word weight achieves the best results on the CQRD_28000 dataset, which is 0.72% higher than the Bi-LSTM model. The method based on the category vector is 2.41% higher than the Bi-LSTM model in the CQRD_8000 dataset. The KD-GCN, although not attaining the optimal outcome, exhibited a superior performance of 3.12% compared to the baseline model. Especially in the minority categories, the CQRD intention classification results have improved significantly. Therefore, the keyword-based reinforcement learning approach can solve the problem of CQRD data imbalance to a certain extent.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **HT** & **WD** & **WWH** & **WHY** & **PRE** & \(p\)**-Value** \\ \hline LSTM & 97.65 * & 83.49 & 92.87 & 81.25 & 85.75 & 0.1140 \\ Bi-LSTM & 97.02 & 86.34 & 93.63 & 81.25 & 87.75 & \\ BERT & 96.97 & 88.29 & 94.63 & 80.42 & 89.16 & 0.2396 \\ ERNIE-Bot & 93.02 & 82.34 & 89.63 & 80.69 & 95.44 * & 0.7096 \\
**KD-LSTM\_weight** & 96.63 & 90.89 & 95.22 * & 84.6 * & 90.03 & 0.01989 \\
**KD-LSTM\_vec** & 96.78 & 91.46 * & 94.50 & 84.17 & 89.17 & 0.07270 \\
**KD-GCN** & 96.93 & 90.22 & 94.55 & 82.71 & 88.96 & 0.07092 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of classification results of different models for specific categories (Accuracy \(\times\) 100). The bolded and highlighted models in the table are the ones we proposed. The asterisk (*) indicates the best result.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Precision** & **Recall** & **F-Score** \\ \hline TF-IDF & 85.33 & 77.58 & 81.27 \\ ERNIE-Bot & 87.32 & 89.91 & 88.60 \\ WF-RF & 87.19 & 90.22 & 88.68 \\
**KD-LSTM\_weight** & 95.12 & 95.63 * & 95.37 \\
**KD-LSTM\_vec** & 95.67 * & 95.23 & 95.45 * \\
**KD-GCN** & 94.69 & 95.17 & 94.93 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Keyword analysis by multiple models (* 100). The bolded and highlighted models in the table are the ones we proposed. The asterisk (*) indicates the best result.  
**Author Contributions**: Conceptualization, H.W. and D.H.; methodology, H.W.; software, H.W.; validation, H.W. and D.H.; formal analysis, H.W.; investigation, H.W.; resources, D.H.; data curation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, H.W. and X.L.; visualization, H.W.; supervision, D.H.; project administration, D.H.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was funded by the Key R&D plans Yunnan Province, grant number 2020203 AA080004 and the National Key R&D Plan, grant number 2020 AAA0108004.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The IC-CDRD dataset is the dataset that we have constructed, and we agree to provide it free of charge to any researchers who require it [[https://github.com/WHnihao/IC-CQRD-Dataset](https://github.com/WHnihao/IC-CQRD-Dataset)]([https://github.com/WHnihao/IC-CQRD-Dataset](https://github.com/WHnihao/IC-CQRD-Dataset)) (accessed on 15 March 2025).  
**Acknowledgments**: This work was supported by the Key R&D Plans Yunnan Province (Approval No.: 202203 AA080004) and the National Key R&D Plan (Approval No.: 2020 AAA0108004).  
**Conflicts of Interest**: All authors declare no conflicts of interest.