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
来源
PROCEEDINGS OF THE THIRTIETH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2021 | 2021年
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.
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收藏
页码:4713 / 4720
页数:8
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