2021/02/28 by He Zhao, Zhao, He, Dinh Phung +9 · 6 citations
Computer Science · Engineering · Social Sciences · #Advanced Text Analysis Techniques #Artificial intelligence #Artificial neural network #Computation and Language (cs.CL) #Computational and Text Analysis Methods #Computer science #Data science #Deep learning #Deep neural networks #Engineering #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Language model #Machine Learning (cs.LG) #Open research #Range (aeronautics) #Topic Modeling #World Wide Web #cs.CL #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2103.00498
published in arXiv (Cornell University) (Cornell University) · A review on Neural Topic Models
arxiv created 2021/02/28 · openalex publication_date 2021/02/28 · arxiv updated 2021/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 over 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 focusing on this specific topic.