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Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data.
Short text topic modeling techniques, applications, and performance: A survey
Jipeng Qiang, Qian Zhenyu, Yun Li, Yunhao Yuan, and Xindong Wu. 2019 · 1904
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Training products of experts by minimizing contrastive divergence
Geoffrey E. Hinton. 2002 · 2002
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Latent dirichlet allocation
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003 · 2003
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What the [MASK]? Making Sense of Language-Specific BERT Models
Debora Nozza, Federico Bianchi, and Dirk Hovy. 2020 · 2003
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Reading tea leaves: How humans interpret topic models
Jonathan Chang, Jordan L. Boyd-Graber, Sean Gerrish, Chong Wang, and David M. Blei. 2009 · 2009
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Replicated softmax: an undirected topic model
Ruslan Salakhutdinov and Geoffrey E. Hinton. 2009 · 2009
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A similarity measure for indefinite rankings
William Webber, Alistair Moffat, and Justin Zobel. 2010 · 2010
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A neural autoregressive topic model
Hugo Larochelle and Stanislas Lauly. 2012 · 2012
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Discovering coherent topics using general knowledge
Zhiyuan Chen, Arjun Mukherjee, Bing Liu, Meichun Hsu, Malú Castellanos, and Riddhiman Ghosh. 2013 · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality
Jey Han Lau, David Newman, and Timothy Baldwin. 2014 · 2014
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Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor. 2014 · 2014
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Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David M. Blei. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Gaussian LDA for topic models with word embeddings
Rajarshi Das, Manzil Zaheer, and Chris Dyer. 2015 · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
Cited alongside, same era.
Improving topic models with latent feature word representations
Dat Quoc Nguyen, Richard Billingsley, Lan Du, and Mark Johnson. 2015 · 2015
Cited alongside, same era.
Exploring the space of topic coherence measures
Michael Röder, Andreas Both, and Alexander Hinneburg. 2015 · 2015
Cited alongside, same era.
Efficient methods for incorporating knowledge into topic models
Coherence-aware neural topic modeling
Ran Ding, Ramesh Nallapati, and Bing Xiang. 2018 · 2018
Later among the works it cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Document informed neural autoregressive topic models with distributional prior
Pankaj Gupta, Yatin Chaudhary, Florian Buettner, and Hinrich Schütze. 2019 · 2019
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Later among the works it cites.
Topic modeling in embedding spaces
Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei. 2020 · 2020
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Yi Yang, Doug Downey, and Jordan L. Boyd-Graber. 2015 · 2015
Cited alongside, same era.
Neural variational inference for text processing
Yishu Miao, Lei Yu, and Phil Blunsom. 2016 · 2016
Cited alongside, same era.
SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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.
Autoencoding variational inference for topic models
Akash Srivastava and Charles Sutton. 2017 · 2017
Cited alongside, same era.
A correlated topic model using word embeddings
Guangxu Xun, Yaliang Li, Wayne Xin Zhao, Jing Gao, and Aidong Zhang. 2017 · 2017
Cited alongside, same era.
Metalda: A topic model that efficiently incorporates meta information
He Zhao, Lan Du, Wray Buntine, and Gang Liu. 2017 · 2017
Cited alongside, same era.
Closest in time.
Neural topic modeling with continual lifelong learning
Pankaj Gupta, Yatin Chaudhary, Thomas Runkler, and Hinrich Schuetze. 2020 · 2020
Closest in time.
Improving Neural Topic Models using Knowledge Distillation
Alexander Miserlis Hoyle, Pranav Goel, and Philip Resnik. 2020 · 2020
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A primer in BERTology: What we know about how BERT works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2020
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Cross-lingual contextualized topic models with zero-shot learning
Federico Bianchi, Silvia Terragni, Dirk Hovy, Debora Nozza, and Elisabetta Fersini. 2021 · 2021
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Fine-tuning encoders for improved monolingual and zero-shot polylingual neural topic modeling
Aaron Mueller and Mark Dredze. 2021 · 2021
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Word embedding-based topic similarity measures
Silvia Terragni, Elisabetta Fersini, and Enza Messina. 2021b · 2021
Closest in time.
Topic modelling meets deep neural networks: A survey
He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, and Wray Buntine. 2021 · 2021
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