# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 1 Introduction  
Text classification is the basic work of natural language processing tasks, and it is also one of the hot spots in its research field. It mainly uses text classification to manage texts effectively and plays an important role in application fields such as information retrieval, public opinion analysis, topic classification, spam screening, and opinion mining. Traditional text classification models are mainly based on machine learning algorithms, such as Support Vector Machines (SVM) [1], naive Bayes [2], etc. These algorithms are relatively mature and have achieved good results. Of course, they also have limitations, such as high-dimensionality, data sparsity and other problems in text classification, insufficient representation of text semantic features, etc. Deep learning methods can extract text features that are richer in semantic information, and it can greatly save the cost of manual intervention. Existing text classification models based on deep neural networks include typical neural network methods (e.g., Convolutional Neural Network (CNN) [3], Recurrent Neural Network (RNN) [4], Capsule Network [5]), and their excellent variants (e.g., Long and Short-Term Memory (LSTM) [6] Network, Gated Recurrent Unit (GRU) [7], Convolutional Recurrent Neural Network (CRNN) [8]), and various models based on attention [9] (e.g., BERT [10]), etc. These deep learning models can well capture the semantic and syntactic information in the local continuous word sequence, but most of them mayignore the information with discontinuous semantic words. Moreover, they are limited in their ability to capture the global features of the information [11]. For example, global word co-occurrence information, global syntactic structure information, etc., and the global dependencies between all words also have a certain guiding role for classification. The Graph Convolutional Neural Network [12] method has been a research hotspot in recent years. This model performs graph convolution operations on the constructed topological relationship graph to obtain features, thereby realizing text classification. In order to solve the classification problem, many variants of GCN [13; 14; 15; 16] have been proposed and explored. Although GCN is gradually becoming a good choice for graph-based text classification, the current research still has some shortcomings that cannot be ignored. In the recent study on text classification, researchers represented text as a graph structure and captured the structural information of the text as well as the discontinuous and long-distance dependencies between words through a Graph Convolutional Network. The most representative work is Graph Convolutional Network (GCN) [12] and its variant Text GCN [13]. This method represents words as nodes and aggregates the neighborhood information of the nodes through an adjacency matrix, thus integrating the global context of domain-specific languages to some extent. When the graph convolutional network model builds text graphs, most of them use the co-occurrence relationship of words and the inclusion relationship between documents and words, resulting in a lack of richer relationship information expression between words and words and the insufficient capture of long-distance dependencies of words in sentences. The measure of a word co-occurrence relationship generally uses normalized point-wise mutual information (NPMI) to calculate the weight between two word nodes. However, the calculation of NPMI depends on the corpus. If some words have a low probability of appearing in the corpus, this method may make the calculation result of NPMI very small, resulting in the determination that the similarity of two words is low or not. In addition, graph convolution based on word co-occurrence relationships and semantic dictionary composition can only capture the relationship of non-continuous entities in the text and cannot simultaneously capture the short-distance and long-distance dependencies of words in sentences.