Fetching the paper…
Reading the bibliography…
Topic models are a useful analysis tool to uncover the underlying themes within document collections.
Classification and clustering of arguments with contextualized word embeddings
Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, and Iryna Gurevych. 2019 · 1906
Earlier work this paper cites.
Topic modeling in embedding spaces
Adji B Dieng, Francisco JR Ruiz, and David M Blei. 2019 · 1907
Earlier work this paper cites.
Mallet: A machine learning for language toolkit
Andrew Kachites McCallum. 2002 · 2002
Earlier work this paper cites.
Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003 · 2003
Earlier work this paper cites.
Language modeling by clustering with word embeddings for text readability assessment
Miriam Cha, Youngjune Gwon, and HT Kung. 2017 · 2006
Earlier work this paper cites.
Normalized (pointwise) mutual information in collocation extraction
Gerlof Bouma. 2009 · 2009
Earlier work this paper cites.
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 · 2011
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Integrating document clustering and topic modeling
Pengtao Xie and Eric P Xing. 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
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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.
Topical word embeddings
Yang Liu, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun. 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.
Unsupervised topic modeling for short texts using distributed representations of words
Vivek Kumar Rangarajan Sridhar. 2015 · 2015
Distributed document and phrase co-embeddings for descriptive clustering
Motoki Sano, Austin J Brockmeier, Georgios Kontonatsios, Tingting Mu, John Y Goulermas, Jun’ichi Tsujii, and Sophia Ananiadou. 2017 · 2017
Later among the works it cites.
A correlated topic model using word embeddings
Guangxu Xun, Yaliang Li, Wayne Xin Zhao, Jing Gao, and Aidong Zhang. 2017 · 2017
Later among the works it cites.
A word embeddings informed focused topic model
He Zhao, Lan Du, and Wray Buntine. 2017 · 2017
Later among the works it cites.
JAX: composable transformations of Python+NumPy programs
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, and Skye Wanderman-Milne. 2018 · 2018
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Nonparametric spherical topic modeling with word embeddings
Kayhan Batmanghelich, Ardavan Saeedi, Karthik Narasimhan, and Sam Gershman. 2016 · 2016
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Applications of topic models
Jordan Boyd-Graber, Yuening Hu, David Mimno, et al. 2017 · 2017
Cited alongside, same era.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Spherical text embedding
Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance Kaplan, and Jiawei Han. 2019 · 2019
Later among the works it cites.
Detecting topics in documents by clustering word vectors
Guilherme Raiol de Miranda, Rodrigo Pasti, and Leandro Nunes de Castro. 2019 · 2019
Later among the works it cites.
Cluwords: exploiting semantic word clustering representation for enhanced topic modeling
Felipe Viegas, Sérgio Canuto, Christian Gomes, Washington Luiz, Thierson Rosa, Sabir Ribas, Leonardo Rocha, and Marcos André Gonçalves. 2019 · 2019
Later among the works it cites.