Word Grounded Graph Convolutional Network
Zhibin Lu Qianqian Xie Benyou Wang Jian-yun Nie
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.