# Heterogeneous Graph Neural Network for Short Text Classification
## 1 Introduction  
Short text usually refers to the text form with a relatively short length, generally no more than 160 characters. Short text classification is a kind of text mining technology and an important research direction in natural language processing (NLP). With the continuous development of NLP, people's requirements for text mining are not limited to ordinary texts, and short texts including literature summaries, news public opinions, and opinion comments have begun to enter the public's field of vision [1]. Analysis of short texts has important application value, such as opinion mining for user comments, topic detection for social media, and public opinion warning [2]. Different from long texts, short texts have the characteristics of sparse sample features, lack of context, and many noisy data [3]. They make the semantics difficult to distinguish, and the comprehension deviation cannot be eliminated, and the results are a huge difference between the semantics of the short text and the results learned by the model. Differences make traditional text classification algorithms not well suited for short texts. In order to solve such problems, relevant scholars have used traditional text classification methods, such as SVM [4], BAYES [5], KNN [6], to achieve short text classification by reducing its discreteness reducing noise features. However, these methods originate from traditional text classification. Through some methods to improve the classification accuracy, there are not the most suitable methods for short text classification. Benefiting from the characteristics of high randomness of Graph Neural Network (GNN) structure and unique node update method, it can make up for the shortcomings of traditional text classification methods in short texts. In recent years, some scholars have begun to study GNN for short text classification [7; 8; 9]. GNN is suitable for data with limited labels and lack of features. Adding edges to defined nodes can better reduce noise, focus on existing features. However, the number of such studies is limited, and the problems faced by short text classification have not been completely solved. Therefore, the research of GNN suitable for short text is very meaningful. This research also faces challenges. Most of the existing short text classification based on GNN rely on external knowledge supplements, such as word vectors or external knowledge bases. Such methods achieve the effect of knowledge supplementation at the expense of increasing the amount of computation, but doing so is not necessarily the best. First, due to different scenarios, pre-trained word vectors may not necessarily improve the effect of text classification, but increase the difficulty of mapping [10]. Second, excessive reliance on external knowledge bases may bring more noise and reduce model accuracy. For example, in the case of relying on an external knowledge base, the word \"apple\" in the two texts \"[PERSON] founded Apple\" and \"[PERSON] thought for a long time under the apple tree\" will be given the same meaning in the knowledge base link. Since short texts do not provide much information, such misleading knowledge can lead to fatal results, and we need a model that focuses more on the characteristics of the text itself. In this paper, we propose a heterogeneous graph convolutional network suitable for short texts. This network does not rely on word vectors or external knowledge bases. It emphasizes the relationship between nodes themselves, and achieves strong classification performance at low computational cost. In summary, the contributions of this paper are as follows:  
1. We propose a heterogeneous graph around text, words, and POS tags, using a multi-layer GCN to learn the features of the three graphs separately, and combine the information of different graphs to learn short texts. 2.