Topic Modelling Meets Deep Neural Networks: A Survey

2021/02/28 by Zhao, He, Phung, Dinh, Huynh, Viet +3 · 3 citations
#Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2103.00498

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 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.

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• Importance-aware Topic Modeling for Discovering Public Transit Risk from Noisy Social Media
• Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming
• SciTopic: Enhancing Topic Discovery in Scientific Literature through Advanced LLM

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