  Graph Convolutional Networks (GCNs) have shown strong performance in learning
text representations for various tasks such as text classification, due to its
expressive power in modeling graph structure data (e.g., a literature citation
network). Most existing GCNs are limited to deal with documents included in a
pre-defined graph, i.e., it cannot be generalized to out-of-graph documents. To
address this issue, we propose to transform the document graph into a word
graph, to decouple data samples (i.e., documents in training and test sets) and
a GCN model by using a document-independent graph. Such word-level GCN could
therefore naturally inference out-of-graph documents in an inductive way. The
proposed Word-level Graph (WGraph) can not only implicitly learning word
presentation with commonly-used word co-occurrences in corpora, but also
incorporate extra global semantic dependency derived from inter-document
relationships (e.g., literature citations). An inductive Word-grounded Graph
Convolutional Network (WGCN) is proposed to learn word and document
representations based on WGraph in a supervised manner. Experiments on text
classification with and without citation networks evidence that the proposed
WGCN model outperforms existing methods in terms of effectiveness and
efficiency.
