# Heterogeneous Graph-Convolution-Network-Based Short-Text Classification
###### Abstract  
With the development of online interactive media platforms, a large amount of short text has appeared on the internet. Determining how to classify these short texts efficiently and accurately is of great significance. Graph neural networks can capture information dependencies in the entire short-text corpus, thereby enhancing feature expression and improving classification accuracy. However, existing works have overlooked the role of entities in these short texts. In this paper, we propose a heterogeneous graph-convolution-network-based short-text classification (SHGCN) method that integrates heterogeneous graph convolutional neural networks of text, entities, and words. Firstly, the model constructs a graph network of the text and extracts entity nodes and word nodes. Secondly, the relationship of the graph nodes in the heterogeneous graphs is determined by the mutual information between the words, the relationship between the documents and words, and the confidence between the words and entities. Then, the feature is represented through a word graph and combined with its BERT embedding, and the word feature is strengthened through BiLstm. Finally, the enhanced word features are combined with the document graph representation features to predict the document categories. To verify the performance of the model, experiments were conducted on the public datasets AGNews, R52, and MR. The classification accuracy of SHGCN reached 88.38%, 93.87%, and 82.87%, respectively, which is superior to that of some existing advanced classification methods. short-text classification; physical information; graph convolution neural network; BERT +
Footnote †: journal: MedicalThe above characteristics of short text make it difficult to apply traditional text classification methods to short-text data. Recently, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to short-text classification. Compared with traditional methods such as logical regression [3] and support vector machines (SVMs) [4], deep learning models can give priority to the local and sequential characteristics of the text and show good results in terms of classification. However, these above-mentioned deep learning models often ignore the global features of the corpus when modeling short text. In recent years, graph neural networks (GNNs) [5] have attracted extensive attention from researchers because they can effectively deal with text structures with complex relationships and retain global word features. For example, the TextGCN model learns the feature representation of text for classification tasks by constructing a heterogeneous graph of the text and words. However, due to the small number of short-text words and the lack of contextual semantic information, TextGCN has a poor classification effect on short-text datasets. In order to solve the problem of the sparse semantic features of short text, this paper proposes a heterogeneous graph-convolution-network-based short-text classification (SHGCN) method, which integrates external entity information, text, and words into the graph neural network to model short text. It not only captures the relationship between text and entities, but also learns the feature representation of text and words. Further, in this model, the learned text representation and word representation are input into Bi-LSTM; as a result of the combination of the BERT word embedding representation, the short text is perfectly classified by the Bi-LSTM model. The experimental results show that SHGCN has a higher detection accuracy compared to other commonly used short-text classification methods. The main innovations of this paper are as follows:  
1. This paper proposes a method of integrating entities into graph-based network modeling of short texts, which can eliminate the ambiguity of some words in the text. 2.