# Heterogeneous Graph Neural Network for Short Text Classification
## 2 Related Work  
**Traditional text classification methods** are K-nearest neighbors (KNN) [11], which classifies an unlabeled sample by finding the class with the most samples over the k closest labeled samples. Decisions tree (DT) is a supervised tree structure induction classification algorithm. It uses the attributes of the samples as nodes and the values of the attributes as the branches of the tree structure. Commonly used decision tree algorithms are ID5 [12], CART [13], C4.5 [14] and so on. [PERSON] et al. [15] proposed to perform context word embedding through word windows of different sizes to calculate the vector representation of multi-scale semantic units in short texts, and then select word representations that are close enough in the semantic units to form a text expansion matrix, and finally pass CNN is used for classification, which reduces the discrete type of short text features to achieve better learning results. [PERSON] et al. [16] aimed at the expansion of the KNN short text classification algorithm, which led to the problem of reducing the efficiency of short text classification, using chi-square statistics to extract training samples in a category that are more similar to the characteristics of the category, and split the training space into finer details. To improve the quality of training samples, the number of texts compared by the KNN short text classification algorithm reduce, thereby improving the efficiency. [PERSON] et al. [17] proposed a method of using an external database containing the text to be classified, using LDA to train a topic model, extracting text topics, and then integrating the topics into the text to achieve text expansion. Using the expanded text training, the resulting vector is classified, so that the error rate is significantly reduced. [PERSON] et al. [18] proposed a combination of a neuronal network, discriminant analysis, SVM, and other methods to identify the author of a given text. This method presents the text as elements of a separable Hilbert space, keep a lot of stylistic information and not requiring a prior reduction of the dimension, enabling special text classification. [PERSON] et al. [19] proposed a network that considers the semantic similarity between paragraphs to improve the text classification task. This method can be combined with traditional networks and is a highly adaptable improvement method. **Graph convolutional neural network** is a kind of GNN. [PERSON] et al. [20] first proposed the concept of GCN in 2014. Graph convolution semi-supervised learning uses convolution operations to combine the feature vectors of nodes with the graph structure between nodes. Every time the feature vectors of nodes undergo a graph convolution operation, they update their own feature vectors with adjacent nodes through the graph structure, so that similar nodes have similar feature vectors [21]. Therefore, GCN have attracted widespread attention, and related scholars introduced them into many fields, such as entity recognition [22], image understanding [23], action recognition [24], and text classification [25]. **Graph neural networks for short text classification** have received more and more attention from scholars, and many models have been proposed based on them. [PERSON] et al. [7] proposed a method to classify short texts based on a heterogeneous graph between words and texts. It puts words and texts in a large global graph, and proposed two kinds of edges to connect them, and learns textual information through an overall GCN. This is one of the first articles to propose such a method, and many subsequent studies are based on it. In order to make up for the lack of short text features, such as the difficulty of capturing and lacking context, [PERSON] et al. [8] proposed a graph convolutional neural network to classify text using attachment relationships and supplementing information with external knowledge bases.