In this work, we propose a novel heterogeneous graph neural network based method for semisupervised short text classification, which makes full use of both limited labeled data and large unlabeled data by allowing information propagation through our automatically constructed graph.
we first present a flexible HIN framework for modeling the short texts, which is able to incorporate any additional information (e.g., entities and topics) as well as capture the rich relations among the texts and the additional information.