Classifying health questions with local semantic and global structural information

Considering the shortcomings of existing research methods in the Chinese medical health questions classification task, this paper proposes a new health questions classification method that incorporates the health questions’ local semantic information and global structural information. We first obtain the questions’ local semantic representation and global structural representation by the convolutional neural network (CNN) and independent recurrent neural network (IndRNN). Then, we extract the questions’ semantic representation, and especially we get the questions’ semantic representation by fusing the local semantic representation and global structural representation using a self-attention mechanism. Finally, we classify the semantic representation of the medical health question through the softmax layer and output classification result. Experimental results show that this method leads to a good performance in the Chinese medical health questions dataset, and that it effectively improves the semantic representation ability of the model and significantly resolves the gradient vanishing and gradient explosion problems.