Topic Modelling Meets Deep Neural Networks: A Survey

被引:0
|
作者
Zhao, He [1 ]
Dinh Phung [1 ,2 ]
Viet Huynh [1 ]
Jin, Yuan [1 ]
Du, Lan [1 ]
Buntine, Wray [1 ]
机构
[1] Monash Univ, Dept Data Sci & Artificial Intelligence, Melbourne, Vic, Australia
[2] VinAI Res, Hanoi, Vietnam
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:4713 / 4720
页数:8
相关论文
共 50 条
  • [1] Hebbian Learning Meets Deep Convolutional Neural Networks
    Amato, Giuseppe
    Carrara, Fabio
    Falchi, Fabrizio
    Gennaro, Claudio
    Lagani, Gabriele
    IMAGE ANALYSIS AND PROCESSING - ICIAP 2019, PT I, 2019, 11751 : 324 - 334
  • [2] A survey on the applications of Deep Neural Networks
    Latha, R. S.
    Sreekanth, G. R. R.
    Suganthe, R. C.
    Selvaraj, R. Esakki
    2021 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS (ICCCI), 2021,
  • [3] A survey of uncertainty in deep neural networks
    Gawlikowski, Jakob
    Tassi, Cedrique Rovile Njieutcheu
    Ali, Mohsin
    Lee, Jongseok
    Humt, Matthias
    Feng, Jianxiang
    Kruspe, Anna
    Triebel, Rudolph
    Jung, Peter
    Roscher, Ribana
    Shahzad, Muhammad
    Yang, Wen
    Bamler, Richard
    Zhu, Xiao Xiang
    ARTIFICIAL INTELLIGENCE REVIEW, 2023, 56 (SUPPL 1) : 1513 - 1589
  • [4] A survey of uncertainty in deep neural networks
    Jakob Gawlikowski
    Cedrique Rovile Njieutcheu Tassi
    Mohsin Ali
    Jongseok Lee
    Matthias Humt
    Jianxiang Feng
    Anna Kruspe
    Rudolph Triebel
    Peter Jung
    Ribana Roscher
    Muhammad Shahzad
    Wen Yang
    Richard Bamler
    Xiao Xiang Zhu
    Artificial Intelligence Review, 2023, 56 : 1513 - 1589
  • [5] A Survey on Fuzzy Deep Neural Networks
    Das, Rangan
    Sen, Sagnik
    Maulik, Ujjwal
    ACM COMPUTING SURVEYS, 2020, 53 (03)
  • [6] Hyperbolic Deep Neural Networks: A Survey
    Peng, Wei
    Varanka, Tuomas
    Mostafa, Abdelrahman
    Shi, Henglin
    Zhao, Guoying
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2022, 44 (12) : 10023 - 10044
  • [7] Survey on Testing of Deep Neural Networks
    Wang Z.
    Yan M.
    Liu S.
    Chen J.-J.
    Zhang D.-D.
    Wu Z.
    Chen X.
    Ruan Jian Xue Bao/Journal of Software, 2020, 31 (05): : 1255 - 1275
  • [8] Deep Neural Networks on Chip - A Survey
    Huo Yingge
    Ali, Imran
    Lee, Kang-Yoon
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP 2020), 2020, : 589 - 592
  • [9] Fractional Calculus Meets Neural Networks for Computer Vision: A Survey
    Coelho, Cecilia
    Costa, M. Fernanda P.
    Ferras, Luis L.
    AI, 2024, 5 (03) : 1391 - 1426
  • [10] DEEP NEURAL NETWORKS FOR PROBABILITY OF DEFAULT MODELLING
    Georgiou, Kyriakos
    Yannacopoulos, Athanasios N.
    JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, 2024, 20 (12) : 3647 - 3677