Self-supervised short text classification with heterogeneous graph neural networks

Short text classification has been a fundamental task in natural language processing, which benefits various applications, such as sentiment analysis, news tagging, and intent recommendation. However, classifying short texts is challenging due to the information sparsity in the text corpus. Besides, the performance of existing machine learning classification models largely relies on sufficient training data, yet labels can be scarce and expensive to obtain in real-world text classification scenarios. In this article, we propose a novel self-supervised short text classification method. Specifically, we first model the short text corpus as a heterogeneous graph to address the information sparsity problem. Then, we introduce a self-attention-based heterogeneous graph neural network model to learn short text embeddings. In addition, we adopt a self-supervised learning framework to exploit internal and external similarities among short texts. Experiments on five real-world short text benchmarks validate the effectiveness of our proposed method compared with the state-of-the-art methods.