Recursive Graphical Neural Networks for Text Classification

Understand

The complicated syntax structure of natural language is hard to be explicitly modeled by sequence-based models.

• Graph is a natural structure to describe the complicated relation between tokens.
• The recent advance in Graph Neural Networks (GNN) provides a powerful tool to model graph structure data, but simple graph models such as Graph Convolutional Networks (GCN) suffer from over-smoothing problem, that is, when stacking multiple layers, all nodes will converge to the same value.
• In this paper, we propose a novel Recursive Graphical Neural Networks model (ReGNN) to represent text organized in the form of graph.