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As one of the prevalent topic mining tools, neural topic modeling has attracted a lot of interests for the advantages of high efficiency in training and strong generalisation abilities.
Latent dirichlet allocation
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003 · 2003
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Finding scientific topics
Thomas L Griffiths and Mark Steyvers. 2004 · 2004
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A web-based kernel function for measuring the similarity of short text snippets
Mehran Sahami and Timothy D. Heilman. 2006 · 2006
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Learning to classify short and sparse text & web with hidden topics from large-scale data collections
Xuan Hieu Phan, Minh Le Nguyen, and Susumu Horiguchi. 2008 · 2008
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Learning a parametric embedding by preserving local structure
Laurens van der Maaten. 2009 · 2009
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Transferring topical knowledge from auxiliary long texts for short text clustering
Ou Jin, Nathan Nan Liu, Kai Zhao, Yong Yu, and Qiang Yang. 2011 · 2011
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Comparing twitter and traditional media using topic models
Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee-Peng Lim, Hongfei Yan, and Xiaoming Li. 2011 · 2011
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Finding bursty topics from microblogs
Qiming Diao, Jing Jiang, Feida Zhu, and Ee-Peng Lim. 2012 · 2012
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A biterm topic model for short texts
Xiaohui Yan, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng. 2013 · 2013
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BTM: topic modeling over short texts
Xueqi Cheng, Xiaohui Yan, Yanyan Lan, and Jiafeng Guo. 2014 · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
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Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality
Jey Han Lau, David Newman, and Timothy Baldwin. 2014 · 2014
Cited alongside, same era.
The dual-sparse topic model: mining focused topics and focused terms in short text
Tianyi Lin, Wentao Tian, Qiaozhu Mei, and Hong Cheng. 2014 · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Cited alongside, same era.
A dirichlet multinomial mixture model-based approach for short text clustering
Reparameterization gradients through acceptance-rejection sampling algorithms
Christian A. Naesseth, Francisco J. R. Ruiz ands Scott W. Linderman, and David M. Blei. 2017 · 2017
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Autoencoding variational inference for topic models
Akash Srivastava and Charles A. Sutton. 2017 · 2017
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Topic memory networks for short text classification
Jichuan Zeng, Jing Li, Yan Song, Cuiyun Gao, Michael R. Lyu, and Irwin King. 2018 · 2018
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Graphbtm: Graph enhanced autoencoded variational inference for biterm topic model
Qile Zhu, Zheng Feng, and Xiaolin Li. 2018 · 2018
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Decoupling sparsity and smoothness in the dirichlet variational autoencoder topic model
Sophie Burkhardt and Stefan Kramer. 2019 · 2019
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Jianhua Yin and Jianyong Wang. 2014 · 2014
Cited alongside, same era.
Topic modeling for short texts with auxiliary word embeddings
Chenliang Li, Haoran Wang, Zhiqian Zhang, Aixin Sun, and Zongyang Ma. 2016 · 2016
Cited alongside, same era.
Neural variational inference for text processing
Yishu Miao, Lei Yu, and Phil Blunsom. 2016 · 2016
Cited alongside, same era.
Discovering discrete latent topics with neural variational inference
Yishu Miao, Edward Grefenstette, and Phil Blunsom. 2017 · 2017
Cited alongside, same era.
Document informed neural autoregressive topic models with distributional prior
Pankaj Gupta, Yatin Chaudhary, Florian Buettner, and Hinrich Schütze. 2019 · 2019
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Sparsemax and relaxed wasserstein for topic sparsity
Tianyi Lin, Zhiyue Hu, and Xin Guo. 2019 · 2019
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Neural variational correlated topic modeling
Luyang Liu, Heyan Huang, Yang Gao, Yongfeng Zhang, and Xiaochi Wei. 2019 · 2019
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User group based emotion detection and topic discovery over short text
Jiachun Feng, Yanghui Rao, Haoran Xie, Fu Lee Wang, and Qing Li. 2020 · 2020
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