Reinforcement Learning-Based Intent Classification of Chinese Questions About Respiratory Diseases

The intent classification of Chinese questions about respiratory diseases (CQRD) can not only promote the development of smart medical care, but also strengthen epidemic surveillance. The major core of the intent classification of CQRD is text representation. This paper studies how to utilize keywords to construct CQRD representation. Based on the characteristics of CQRD, we propose a keywords-based reinforcement learning model. In the reinforcement learning model based on keywords, we crafted a word frequency reward function to aid in generating the reward function and determining keyword categories. Simultaneously, to generate CQRD representations using keywords, we developed two models: keyword-driven LSTM (KD-LSTM) and keyword-driven GCN (KD-GCN). The KD-LSTM incorporates two methods: one based on word weights and the other based on category vectors. The KD-GCN employs keywords to construct a weight matrix for training. 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 category vector outperforms the Bi-LSTM model in the CQRD_8000 dataset by 2.41%. The KD-GCN, although not attaining the optimal outcome, exhibited a superior performance of 3.12% compared to the GCN model. Both methods have significantly improved the classification results of minority classes.