Neural Topic Modeling by Incorporating Document Relationship Graph
Deyu Zhou, Xuemeng Hu, Rui Wang · 2020
Graph Neural Networks (GNNs) that capture the relationships between graph nodes via message passing have been a hot research direction in the natural language processing community.In this paper, we propose Graph Topic Model (GTM), a GNN based neural topic model that represents a corpus as a document relationship graph.Documents and words in the corpus become nodes in the graph and are connected based on document-word cooccurrences.By introducing the graph structure, the relationships between documents are established through their shared words and thus the topical representation of a document is enriched by aggregating information from its neighboring nodes using graph convolution.Extensive experiments on three datasets were conducted and the results demonstrate the effectiveness of the proposed approach.