Short text classification has found rich and critical applications in news and tweet tagging to
help users find relevant information. Due to lack of labeled training data in many practical
use cases, there is a pressing need for studying semi-supervised short text classification.
Most existing studies focus on long texts and achieve unsatisfactory performance on short
texts due to the sparsity and limited labeled data. In this paper, we propose a novel
heterogeneous graph neural network based method for semi-supervised short text …

Short text classification has been widely explored in news tagging to provide more efficient
search strategies and more effective search results for information retrieval. However, most
existing studies, concentrating on long text classification, deliver unsatisfactory performance
on short texts due to the sparsity issue and the insufficiency of labeled data. In this article, we
propose a novel heterogeneous graph neural network-based method for semi-supervised
short text classification, leveraging full advantage of limited labeled data and large …