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        <title>Neural Topic Modeling by Incorporating Document Relationship Graph</title>
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        <namePart type="given">Deyu</namePart>
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        <namePart type="given">Xuemeng</namePart>
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    <abstract>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 co-occurrences. 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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.310</identifier>
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        <date>2020-nov</date>
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%0 Conference Proceedings
%T Neural Topic Modeling by Incorporating Document Relationship Graph
%A Zhou, Deyu
%A Hu, Xuemeng
%A Wang, Rui
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F zhou-etal-2020-neural
%X 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 co-occurrences. 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.
%R 10.18653/v1/2020.emnlp-main.310
%U https://aclanthology.org/2020.emnlp-main.310
%U https://doi.org/10.18653/v1/2020.emnlp-main.310
%P 3790-3796

Markdown (Informal)

[Neural Topic Modeling by Incorporating Document Relationship Graph](https://aclanthology.org/2020.emnlp-main.310) (Zhou et al., EMNLP 2020)

• Neural Topic Modeling by Incorporating Document Relationship Graph (Zhou et al., EMNLP 2020)

ACL

• Deyu Zhou, Xuemeng Hu, and Rui Wang. 2020. Neural Topic Modeling by Incorporating Document Relationship Graph. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3790–3796, Online. Association for Computational Linguistics.