# Heterogeneous Graph-Convolution-Network-Based Short-Text Classification
## 2 Related Work  
Short-text classification aims to select appropriate labels for a large number of unmarked texts. The existing text classification methods can be divided into three categories: static-based methods, deep learning methods, and graph-neural-network-based methods. A statistical text classification algorithm needs the design and classification algorithm of feature engineering. Feature engineering processes the text data, extracts them as features, and uses them as the input of subsequent classifiers. Usually, the word bag model [6] is used to obtain the data features. In addition, there are some complex text feature projects. For example, the n-gram model proposed by [PERSON] et al. [7] is based on the algorithm of statistical language, which divides the text into a byte fragment sequence (also named a gram) according to the sliding window with the length of n-grams that occur more frequently from a list, which serves as the feature vector space of the text. The topic model proposed by [PERSON] et al. [8] combines the n-gram model with potential topic variables,forming a hierarchical Dirchlet binary generation model that can more accurately generate potential topics that are not affected by function words and obtain a deep representation of the text. Classification algorithms generally include logical regression (LR), support vector machine (SVM), gradient lifting decision tree (GLDT) [9], etc. But text feature engineering often relies on human processing data. For massive data, the cost is too high, the processing time is too long, the text representation obtained by traditional methods has the characteristics of high dimensionality and high sparsity, and the feature expression ability is weak, which is not conducive to classification tasks. Deep learning has made a breakthrough in the field of short-text classification. Compared with traditional text classification algorithms, text classification algorithms based on deep learning can learn deeper and more complex features of text and can achieve end-to-end processing by automatically extracting text features, eliminating complicated manual operations. TextCNN, proposed by [PERSON] et al. [5], applied a convolutional neural network to text classification tasks to capture local information between texts through multiple convolutional cores. The TextRNN proposed by [10] could capture the context semantic relationship of the sequence as long as possible. However, in the training process of the RNN, the gradient would disappear, and thus, it is hard for the long-distance sequence information to be learned. To solve the problem of sparse short-text data, [PERSON] et al. [11] proposed a topic memory network that did not rely on external corpora; it could find keywords for classification through the word co-occurrence feature of the entire dataset and mine potential topics for classification. [PERSON] et al. [12] proposed a model of a convolutional neural network based on knowledge-powered attention with a similarity matrix (KASM) that used a knowledge map (KG) to enrich the semantic representation of short text. These methods only modeled the local information of the text, without paying attention to the global information of the text. In recent years, graph neural networks [13] have been applied to text classification tasks. [PERSON] et al. [14] proposed a text graph convolutional network (Text GCN) that establishes a heterogeneous text graph for the entire corpus through word co-occurrence information and document word relationships, after which the representations of the document and word nodes are learned. Tensor GCN [15] was proposed on the basis of TextGCN. Heterogeneous graphs were built based on semantics, syntax, and sequence, and node information could be spread between graphs. [PERSON] et al. [16] proposed a heterogeneous graph attention network classification model that takes text, topics, and entities as nodes and constructs edges between text nodes and topic nodes, text nodes and entity nodes, and entity nodes and entity nodes.