Topic modelling meets deep neural networks: a survey

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

30 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the Thirtieth International Joint Conference on Artificial Intelligence
EditorsZhi-Hua Zhou
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages4713-4720
Number of pages8
ISBN (Electronic)9780999241196
DOIs
Publication statusPublished - 2021
EventInternational Joint Conference on Artificial Intelligence 2021 - Virtual, Montreal, Canada
Duration: 19 Aug 202127 Aug 2021
Conference number: 30th
https://www.ijcai.org/proceedings/2021/ (Proceedings)
https://ijcai-21.org (Website)

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
ISSN (Print)1045-0823

Conference

ConferenceInternational Joint Conference on Artificial Intelligence 2021
Abbreviated titleIJCAI 2021
Country/TerritoryCanada
CityMontreal
Period19/08/2127/08/21
Internet address

Keywords

  • Knowledge representation and reasoning
  • Machine learning
  • Natural language processing

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