# A Novel Neural Network-Based Method for Medical Text Classification
## 2 Related Work
### Text Classification in General Domain  
The traditional text classification mainly uses machine learning methods. Text is first represented as a vector by use of the feature engineering, and then through the machine learning algorithm to realize the classification process. In the past, the most classical feature engineering is bag-of-words (BOW) [8]. In deep learning, convolutional neural networks are widely used in text classification. [PERSON] represents the text as a 4-dimensional tensor format of the image and inputs it into a simple convolutional neural network for text classification. The experimental results show that the classification performance of the convolutional neural network method is higher than the traditional machine learning method [9]. Based on this, a large number of researchers have made improvements to the convolutional neural network. For the word order problem of text, [PERSON] et al. add the word order to the convolutional neural network to increase the context information of the text [10]. Since the performance of the neural network is greatly affected by the parameter adjustment, [PERSON] et al. initialize the filter used in the convolution operation. In this process, the n-grams are extracted from the train data and clustered by k-means. The experimental results show that the method effectively reduces the error caused by parameter adjustment [11]. [PERSON] et al. propose a deep pyramid convolutional neural network model, which improves the classification accuracy by deepening the hierarchical structure of convolutional neural networks. Experimental results show that the method has good classification performance when the number of data is large [12].  
In addition to convolutional neural networks, recurrent neural networks are widely used in text classification because of their natural sequence structure, which is suitable for natural language processing. However, there is a well-known problem with recurrent neural networks, that is, when  
Figure 1: Gated recurrent unit.  
the length of the text sequence is too long, the model is prone to gradient disappearance or gradient explosion. Based on this, many researchers have proposed improvements. [PERSON] presents a novel model named disconnected recurrent neural network (DRNN), which incorporates position-invariance of CNN into RNN. By limiting the distance of information flow in RNN, the hidden state at each time step is restricted to represent words near the current position [13]. [PERSON] et al. use the multitask learning framework to propose three different shared information mechanisms to model text with task-specific and shared layers [14]. In addition, many researchers combine CNN and RNN. [PERSON] et al. apply a recurrent structure to capture contextual information when learning word representations and then employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the key components in texts [15]. [PERSON] et al. propose a C-LSTM model, which utilizes CNN to extract a sequence of higher-level phrase representations and are fed into a long short-term memory recurrent neural network (LSTM) to obtain the sentence representation [16].