  Text classification is a primary task in natural language processing (NLP).
Recently, graph neural networks (GNNs) have developed rapidly and been applied
to text classification tasks. As a special kind of graph data, the tree has a
simpler data structure and can provide rich hierarchical information for text
classification. Inspired by the structural entropy, we construct the coding
tree of the graph by minimizing the structural entropy and propose HINT, which
aims to make full use of the hierarchical information contained in the text for
the task of text classification. Specifically, we first establish a dependency
parsing graph for each text. Then we designed a structural entropy minimization
algorithm to decode the key information in the graph and convert each graph to
its corresponding coding tree. Based on the hierarchical structure of the
coding tree, the representation of the entire graph is obtained by updating the
representation of non-leaf nodes in the coding tree layer by layer. Finally, we
present the effectiveness of hierarchical information in text classification.
Experimental results show that HINT outperforms the state-of-the-art methods on
popular benchmarks while having a simple structure and few parameters.
