Word Grounded Graph Convolutional Network

• URL: http://arxiv.org/abs/2305.06434v1
• Date: Wed, 10 May 2023 19:56:55 GMT
• Title: Word Grounded Graph Convolutional Network
• Authors: Zhibin Lu, Qianqian Xie, Benyou Wang, Jian-yun Nie
• Abstract summary: Graph Convolutional Networks (GCNs) have shown strong performance in learning text representations for various tasks such as text classification. We propose to transform the document graph into a word graph, to decouple data samples and a GCN model by using a document-independent graph. 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.
• Score: 24.6338889954789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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