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

Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks