  Short text classification is a fundamental task in natural language
processing. It is hard due to the lack of context information and labeled data
in practice. In this paper, we propose a new method called SHINE, which is
based on graph neural network (GNN), for short text classification. First, we
model the short text dataset as a hierarchical heterogeneous graph consisting
of word-level component graphs which introduce more semantic and syntactic
information. Then, we dynamically learn a short document graph that facilitates
effective label propagation among similar short texts. Thus, compared with
existing GNN-based methods, SHINE can better exploit interactions between nodes
of the same types and capture similarities between short texts. Extensive
experiments on various benchmark short text datasets show that SHINE
consistently outperforms state-of-the-art methods, especially with fewer
labels.
