## 2 Literacy Review
### Artificial Neural Network (ANN)  
Inspired by research on neurons in biology, a model imitating the operation of neurons in the brain was created, called the Artificial Neural Network (ANN), also named the Neural Network. It is the classification of supervised learning in the fields of machine learning and deep learning. The Artificial Neural Network basically consists of an input layer, a hidden layer, and an output layer. Similar to the operation of the biological neural system, when data are transmitted to the neural network, the input layer would largely receive nonlinear information, called neurons; each neuron has the activation function of judging the delivery of data to the next-layer neuron. The hidden layer mainly receives and calculates the weight and deviation value and transmits the results to the output layer. The output layer conducts the analysis, calculation, and weighing according to training rules to eventually form the output result. In order to present human knowledge and behavior, the Artificial Neural Network would provide machines with decision-making and judgment abilities through large amounts of calculation and learning to find the best solution in any problem space. Backpropagation (BP) is the most common algorithm in the training of Artificial Neural Networks; the idea is to repeatedly calculate the result of each neuron and use the calculated deviation as the reference for adjusting the weight to achieve more accurate performance. As backpropagation requires a longer training time, [PERSON] et al. [21] proposed the Deep Belief Network (DBN) to solve the problem. The shortened training time and enhancement of technology and hardware of the Artificial Neural Network allowed the emergence of the Deep Neural Network (DNN) to break through the dilemma in past neural network layers and match various neural network layers of the Deep Neural Network, which was also called deep learning. Based on the development of the Artificial Neural Network, the application of deep learning is gradually developed for machine learning. There are several types of deep learning structures presently, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long-Short Term Memory (LSTM) extended from Recurrent Neural Networks. Deep learning is broadly applied to various fields, such as in the field of computer vision [22] to deal with image classification, image recognition, and voice and music recognition [23]. The application to Nature Language Processing has been attempted in recent years [24; 25; 26] for text classification and prediction. CNN and RNN are the most common neural network structures in text classification, and LSTM is the extension of RNN. CNN generally contains a series of convolutional layers and pooling layers as the hidden layer. CNN, in the text classification task, transforms various words in a sentence into the corresponding embedding to obtain the same vectors and form a matrix, or a 2D matrix that deals with matrices in the same way it deals with images, to build a model with CNN. CNN allows for simultaneously viewing a set of words with the context window to comprehend the sentence through word viewing and the combination of various consecutive words [27]. RNN is a time-sequence-related neural network and could precede the modeling and analysis of sequence data. In text data, language presents continuity; the sequence of each word would affect the semantics, meaning RNN is particularly suitable for dealing with text classification, as well as generating texts to predict the next word or character by reading previous context [28]. Although RNN presents a good ability and commonality, it cannot remember and address lengthy context. [PERSON] and [PERSON] [29] therefore proposed Long-Short Term Memory. LSTM is a special time-recursive neural network suitable for predicting a time-sequential data model. In comparison with RNN, LSTM has a cell state to memorize long-term information and presents better performance with longer sequences.