Abstract：Medical question appeal classification can be dealt with as a text classification issue. This paper presents a reinforcement learning method for medical question appeal classification. Firstly, the keywords in medical problems are automatically identified by reinforcement learning, and the keywords and non-keywords in medical problems are assigned different values to constitute a vector. Secondly, the vector is set as the weight vector of the attention mechanism, and the weighted sum of the hidden layer generated by the Bi-LSTM model constitute question representation. At last, the question representation is classified by softmax classifier. Experimental results show that the accuracy of this method outperforms Bi-LSTM model by 1.49%.
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2021, Vol. 35 
