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Topic Modelling Meets Deep Neural Networks: A Survey

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arxiv 2103.00498 v1 pith:R6ZUDOM4 submitted 2021-02-28 cs.LG cs.CLcs.IR

classification cs.LGcs.CLcs.IR
keywords topicneuralmodelsmodellingresearchareadeeplanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

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  1. Agentic Retrieval of Topics and Insights from Earnings Calls

    cs.LG 2025-07 conditional novelty 6.0 of 10

    An LLM agent extracts financial topics from earnings calls, builds a hierarchical topic ontology, and uses topic mention trends to flag rising and falling themes.

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