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Topic modeling is used for discovering latent semantic structure, usually referred to as topics, in a large collection of documents.
Nearest neighbour searches and the curse of dimensionality
RB Marimont and MB Shapiro · 1979
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Learning distributed representations of concepts
Geoffrey E Hinton et al · 1986
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Word association norms, mutual information, and lexicography
Kenneth Ward Church and Patrick Hanks · 1990
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Elements of information theory
M Cover Thomas and A Thomas Joy · 1991
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Probabilistic latent semantic indexing
Thomas Hofmann · 1999
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Latent dirichlet allocation
David M. Blei, Andrew Y. Ng, and Michael I. Jordan · 2003
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A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin · 2003
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An information-theoretic perspective of tf–idf measures
Akiko Aizawa · 2003
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J.R. Firth, 1957, papers in linguistics 1934–51
Henry Widdowson · 2007
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Topics in semantic representation
Thomas L Griffiths, Mark Steyvers, and Joshua B Tenenbaum · 2007
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Syntactic topic models
Jordan L Boyd-Graber and David M Blei · 2009
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Software Framework for Topic Modelling with Large Corpora
Radim Řehůřek and Petr Sojka · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Efficient estimation of word representations in vector space, 2013
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 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
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Linguistic regularities in continuous space word representations
Tomáš Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013
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Linguistic regularities in continuous space word representations
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Exploring topic discriminating power of words in latent dirichlet allocation
Kai Yang, Yi Cai, Zhenhong Chen, Ho-fung Leung, and Raymond Lau · 2016
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An empirical evaluation of doc2vec with practical insights into document embedding generation
Jey Han Lau and Timothy Baldwin · 2016
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Full-text or abstract? examining topic coherence scores using latent dirichlet allocation
S. Syed and M. Spruit · 2017
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Accelerated hierarchical density based clustering
Leland McInnes and John Healy · 2017
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hdbscan: Hierarchical density based clustering
Leland McInnes, John Healy, and Steve Astels · 2017
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Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013
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Density-based clustering based on hierarchical density estimates
Ricardo JGB Campello, Davoud Moulavi, and Jörg Sander · 2013
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Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Distributed representations of sentences and documents
Quoc V. Le and Tomas Mikolov · 2014
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Improving distributional similarity with lessons learned from word embeddings
Omer Levy, Yoav Goldberg, and Ido Dagan · 2015
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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Umap: Uniform manifold approximation and projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Grossberger · 2018
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Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach
Jamaal Hay Wenpeng Yin and Dan Roth · 2019
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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning · 2020
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