Proceedings

Layer-Assisted Neural Topic Modeling over Document Networks

Yiming Wang, Ximing Li, Jihong Ouyang

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3148-3154.

https://doi.org/10.24963/ijcai.2021/433

Keywords

  • Machine Learning: Learning Graphical Models
  • Uncertainty in AI: Graphical Models
  • Uncertainty in AI: Bayesian Networks

Abstract

Neural topic modeling provides a flexible, efficient, and powerful way to extract topic representations from text documents. Unfortunately, most existing models cannot handle the text data with network links, such as web pages with hyperlinks and scientific papers with citations. To resolve this kind of data, we develop a novel neural topic model , namely Layer-Assisted Neural Topic Model (LANTM), which can be interpreted from the perspective of variational auto-encoders. Our major motivation is to enhance the topic representation encoding by not only using text contents, but also the assisted network links. Specifically, LANTM encodes the texts and network links to the topic representations by an augmented network with graph convolutional modules, and decodes them by maximizing the likelihood of the generative process. The neural variational inference is adopted for efficient inference. Experimental results validate that LANTM significantly outperforms the existing models on topic quality, text classification and link prediction..

Yiming Wang, Ximing Li, and Jihong Ouyang. 2021. Layer-Assisted Neural Topic Modeling over Document Networks. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3148–3154. https://doi.org/10.24963/ijcai.2021/433
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