  Text classification is fundamental in natural language processing (NLP), and
Graph Neural Networks (GNN) are recently applied in this task. However, the
existing graph-based works can neither capture the contextual word
relationships within each document nor fulfil the inductive learning of new
words. In this work, to overcome such problems, we propose TextING for
inductive text classification via GNN. We first build individual graphs for
each document and then use GNN to learn the fine-grained word representations
based on their local structures, which can also effectively produce embeddings
for unseen words in the new document. Finally, the word nodes are aggregated as
the document embedding. Extensive experiments on four benchmark datasets show
that our method outperforms state-of-the-art text classification methods.
