Topic Modelling Meets Deep Neural Networks: A Survey

Zhao, He; Phung, Dinh; Huynh, Viet; Jin, Yuan; Du, Lan; Buntine, Wray

doi:10.24963/ijcai.2021/638

Topic Modelling Meets Deep Neural Networks: A Survey

He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, Wray Buntine

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4713-4720.

https://doi.org/10.24963/ijcai.2021/638

Keywords

Machine learning: General
Natural language processing: General
Knowledge representation and reasoning: General

Abstract

Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popular research area, neural topic models, with nearly a hundred models developed and a wide range of applications in neural language understanding such as text generation, summarisation and language models. There is a need to summarise research developments and discuss open problems and future directions. In this paper, we provide a focused yet comprehensive overview of neural topic models for interested researchers in the AI community, so as to facilitate them to navigate and innovate in this fast-growing research area. To the best of our knowledge, ours is the first review on this specific topic.

He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, and Wray Buntine. 2021. Topic Modelling Meets Deep Neural Networks: A Survey. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4713–4720. https://doi.org/10.24963/ijcai.2021/638

@inproceedings{ijcai2021p638,
  title     = {Topic Modelling Meets Deep Neural Networks: A Survey},
  author    = {Zhao, He and Phung, Dinh and Huynh, Viet and Jin, Yuan and Du, Lan and Buntine, Wray},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4713--4720},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/638},
  url       = {https://doi.org/10.24963/ijcai.2021/638},
}

TY  - CPAPER
TI  - Topic Modelling Meets Deep Neural Networks: A Survey
AU  - Zhao, He
AU  - Phung, Dinh
AU  - Huynh, Viet
AU  - Jin, Yuan
AU  - Du, Lan
AU  - Buntine, Wray
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4713
EP  - 4720
DO  - 10.24963/ijcai.2021/638
UR  - https://doi.org/10.24963/ijcai.2021/638
ER  -