IJCAI 2021poster173 citations

Topic Modelling Meets Deep Neural Networks: A Survey

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

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.

Knowledge representation and reasoning: GeneralMachine learning: GeneralNatural language processing: General
BibTeX
@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    = {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},
}
Topic Modelling Meets Deep Neural Networks: A Survey · IJCAI 2021