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Topic model evaluation, like evaluation of other unsupervised methods, can be contentious.
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Henry Berthold Mann and Donald Ransom Whitney. 1947 · 1947
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Human Behaviour and the Principle of Least Effort
George K. Zipf. 1949 · 1949
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A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability
Donald J Schuirmann. 1987 · 1987
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Improving Ratings: Audit in the british university system
Marilyn Strathern. 1997 · 1997
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Power by simulation
Alan H. Feiveson. 2002 · 2002
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MALLET: A machine learning for language toolkit
Andrew Kachites McCallum. 2002 · 2002
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Latent Dirichlet Allocation
David M. Blei, Andrew 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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Content Analysis: an Introduction to its Methodology
Klaus Krippendorff. 2004 · 2004
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The New York Times annotated corpus
Evan Sandhaus. 2008 · 2008
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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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Automatic evaluation of topic coherence
David Newman, Jey Han Lau, Karl Grieser, and Timothy Baldwin. 2010 · 2010
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Software Framework for Topic Modelling with Large Corpora
Radim Řehůřek and Petr Sojka. 2010 · 2010
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Testing Statistical Hypotheses of Equivalence and Noninferiority
Stefan Wellek. 2010 · 2010
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Sparse additive generative models of text
Jacob Eisenstein, Amr Ahmed, and Eric P. Xing. 2011 · 2011
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Optimizing semantic coherence in topic models
David Mimno, Hanna Wallach, Edmund Talley, Miriam Leenders, and Andrew McCallum. 2011 · 2011
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Literature is not data: Against digital humanities
Stephen Marche. 2012 · 2012
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The digital humanities contribution to topic modeling
Elijah Meeks and Scott B Weingart. 2012 · 2012
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Words alone: Dismantling topic models in the humanities
Benjamin M Schmidt. 2012 · 2012
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Evaluating topic coherence using distributional semantics
Nikolaos Aletras and Mark Stevenson. 2013 · 2013
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Text as data: The promise and pitfalls of automatic content analysis methods for political texts
Justin Grimmer and Brandon M Stewart. 2013 · 2013
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Introduction—topic models: What they are and why they matter
John W. Mohr and Petko Bogdanov. 2013 · 2013
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Computer-assisted content analysis : Topic models for exploring multiple subjective interpretations
Jason Chuang, John D. Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon M. Stewart, Margaret E. Roberts, Forough Poursabzi-Sangdeh, Justin Grimmer, Leah Findlater, Jordan Boyd-Graber, and Jeff Heer. 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
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Improving topic models with latent feature word representations
Dat Quoc Nguyen, Richard Billingsley, Lan Du, and Mark Johnson. 2015 · 2015
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Exploring the space of topic coherence measures
Michael Röder, Andreas Both, and Alexander Hinneburg. 2015 · 2015
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Neoliberal tools (and archives): A political history of digital humanities
Daniel Allington, Sarah Brouillette, and David Golumbia. 2016 · 2016
Cited alongside, same era.
Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio. 2016 · 2016
Cited alongside, same era.
How many words do we know? Practical estimates of vocabulary size dependent on word definition, the degree of language input and the participant’s age
Marc Brysbaert, Michaël Stevens, Paweł Mandera, and Emmanuel Keuleers. 2016 · 2016
Cited alongside, same era.
An overview of topic modeling and its current applications in bioinformatics
Lin Liu, Lin Tang, Wen Dong, Shaowen Yao, and Wei Zhou. 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.
Show your work: Improved reporting of experimental results
Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, and Noah A. Smith. 2019 · 2019
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Neural topic model with reinforcement learning
Lin Gui, Jia Leng, Gabriele Pergola, Yu Zhou, Ruifeng Xu, and Yulan He. 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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Cross-referencing using fine-grained topic modeling
Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Emily Hales, and Kevin Seppi. 2019 · 2019
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Topic modeling with Wasserstein autoencoders
Feng Nan, Ran Ding, Ramesh Nallapati, and Bing Xiang. 2019 · 2019
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Alexandra Schofield and David Mimno. 2016 · 2016
Cited alongside, same era.
An automatic approach for document-level topic model evaluation
Shraey Bhatia, Jey Han Lau, and Timothy Baldwin. 2017 · 2017
Cited alongside, same era.
Applications of Topic Models
Jordan Boyd-Graber, Yuening Hu, and David Mimno. 2017 · 2017
Cited alongside, same era.
Continuous semantic topic embedding model using variational autoencoder
Namkyu Jung and Hyeong In Choi. 2017 · 2017
Cited alongside, same era.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 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.
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With little power comes great responsibility
Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, and Dan Jurafsky. 2020 · 2020
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Topic modeling in embedding spaces
Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei. 2020 · 2020
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Utility is in the eye of the user: A critique of NLP leaderboard design
Kawin Ethayarajh and Dan Jurafsky. 2020 · 2020
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Context reinforced neural topic modeling over short texts
Jiachun Feng, Zusheng Zhang, Cheng Ding, Yanghui Rao, and Haoran Xie. 2020 · 2020
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spaCy: Industrial-strength Natural Language Processing in Python
Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020 · 2020
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Improving Neural Topic Models using Knowledge Distillation
Alexander Miserlis Hoyle, Pranav Goel, and Philip Resnik. 2020 · 2020
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Neural topic modeling with cycle-consistent adversarial training
Xuemeng Hu, Rui Wang, Deyu Zhou, and Yuxuan Xiong. 2020 · 2020
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Tree-Structured Neural Topic Model
Masaru Isonuma, Junichiro Mori, Danushka Bollegala, and Ichiro Sakata. 2020 · 2020
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Dirichlet variational autoencoder
Weonyoung Joo, Wonsung Lee, Sungrae Park, and Il-Chul Moon. 2020 · 2020
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Copula guided neural topic modelling for short texts
Lihui Lin, Hongyu Jiang, and Yanghui Rao. 2020 · 2020
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Nonparametric topic modeling with neural inference
Xuefei Ning, Y. Zheng, Zhuxi Jiang, Y. Wang, H. Yang, and J. Huang. 2020 · 2020
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TAN-NTM: Topic attention networks for neural topic modeling
Madhur Panwar, Shashank Shailabh, Milan Aggarwal, and Balaji Krishnamurthy. 2020 · 2020
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A discrete variational recurrent topic model without the reparametrization trick
Mehdi Rezaee and Francis Ferraro. 2020 · 2020
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Learning to summarize from human feedback
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan J. Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano. 2020 · 2020
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Topic modeling with contextualized word representation clusters
Laure Thompson and D. Mimno. 2020 · 2020
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Learning VAE-LDA models with rounded reparameterization trick
Runzhi Tian, Yongyi Mao, and Richong Zhang. 2020 · 2020
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Graph attention topic modeling network
Liang Yang, Fan Wu, Junhua Gu, Chuan Wang, Xiaochun Cao, Di Jin, and Yuanfang Guo. 2020 · 2020
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Neural topic modeling by incorporating document relationship graph
Deyu Zhou, Xuemeng Hu, and Rui Wang. 2020 · 2020
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Pre-training is a hot topic: Contextualized document embeddings improve topic coherence
Federico Bianchi, Silvia Terragni, and Dirk Hovy. 2021 · 2021
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Topic model or topic twaddle? Re-evaluating semantic interpretability measures
Caitlin Doogan and Wray Buntine. 2021 · 2021
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Topic modeling and text analysis for qualitative policy research
Karoliina Isoaho, Daria Gritsenko, and Eetu Mäkelä. 2021 · 2021
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OCTIS: Comparing and optimizing topic models is simple!
Silvia Terragni, Elisabetta Fersini, Bruno Giovanni Galuzzi, Pietro Tropeano, and Antonio Candelieri. 2021 · 2021
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