In this paper, we propose a novel heterogeneous graph neural network based method for semisupervised short text classification, which takes full advantage of both limited labeled and large unlabeled data by information propagation.
we first present a flexible HIN framework for modeling the short texts, which can integrate any additional information and capture their rich relations to address the semantic sparsity of short texts.