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Graph neural networks have triggered a resurgence of graph-based text classification methods, defining today's state of the art.
Graph star net for generalized multi-task learning
Haonan Lu, Seth H. Huang, Tian Ye, and Xiuyan Guo. 2019 · 1906
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Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Approximation by superpositions of a sigmoidal function
George Cybenko. 1989 · 1989
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Keygraph: Automatic indexing by co-occurrence graph based on building construction metaphor
Yukio Ohsawa, Nels E. Benson, and Masahiko Yachida. 1998 · 1998
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Latent dirichlet allocation
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2001 · 2001
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Comparing BERT against traditional machine learning text classification
Santiago González-Carvajal and Eduardo C. Garrido-Merchán. 2020 · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Combine convolution with recurrent networks for text classification
Shengfei Lyu and Jiaqi Liu. 2020 · 2006
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A survey on text classification: From shallow to deep learning
Qian Li, Hao Peng, Jianxin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, and Lifang He. 2020 · 2008
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Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler. 2020 · 2009
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Automatic keyword extraction from individual documents
Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010 · 2010
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Distributed representations of words and phrases and their compositionality
Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
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A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014 · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Recurrent convolutional neural networks for text classification
Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. 2015 · 2015
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PTE: predictive text embedding through large-scale heterogeneous text networks
Jian Tang, Meng Qu, and Qiaozhu Mei. 2015 · 2015
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, et al. 2016 · 2016
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Bayesian performance comparison of text classifiers
Dell Zhang, Jun Wang, Emine Yilmaz, Xiaoling Wang, and Yuxin Zhou. 2016 · 2016
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Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling
Peng Zhou, Zhenyu Qi, Suncong Zheng, Jiaming Xu, Hongyun Bao, and Bo Xu. 2016 · 2016
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
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Using titles vs. full-text as source for automated semantic document annotation
Lukas Galke, Florian Mai, Alan Schelten, Dennis Brunsch, and Ansgar Scherp. 2017 · 2017
Cited alongside, same era.
Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Graph Representation Learning
William L. Hamilton. 2020 · 2020
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TinyBERT: Distilling BERT for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2020 · 2020
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Tensor graph convolutional networks for text classification
Xien Liu, Xinxin You, Xiao Zhang, Ji Wu, and Ping Lv. 2020 · 2020
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Deep double descent: Where bigger models and more data hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever. 2020 · 2020
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MobileBERT: a compact task-agnostic BERT for resource-limited devices
Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. 2020 · 2020
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A survey on text classification and its applications
Xujuan Zhou, Raj Gururajan, Yuefeng Li, Revathi Venkataraman, Xiaohui Tao, Ghazal Bargshady, Prabal Datta Barua, and Srinivas Kondalsamy-Chennakesavan. 2020 · 2020
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Florian Mai, Lukas Galke, and Ansgar Scherp. 2018 · 2018
Cited alongside, same era.
Towards understanding the role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro. 2018 · 2018
Cited alongside, same era.
Large-scale hierarchical text classification with recursively regularized deep graph-cnn
Hao Peng, Jianxin Li, Yu He, Yaopeng Liu, Mengjiao Bao, Lihong Wang, Yangqiu Song, and Qiang Yang. 2018 · 2018
Cited alongside, same era.
Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Cited alongside, same era.
Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms
Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, and Lawrence Carin. 2018 · 2018
Cited alongside, same era.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Sentiment analysis by capsules
Yequan Wang, Aixin Sun, Jialong Han, Ying Liu, and Xiaoyan Zhu. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
A survey on data augmentation for text classification
Markus Bayer, Marc-André Kaufhold, and Christian Reuter. 2021 · 2021
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
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Mostafa Dehghani, Anurag Arnab, Lucas Beyer, Ashish Vaswani, and Yi Tay. 2021 · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
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GNNAutoScale: Scalable and expressive graph neural networks via historical embeddings
Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Jure Leskovec. 2021 · 2021
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A practical survey on faster and lighter transformers
Quentin Fournier, Gaétan Marceau Caron, and Daniel Aloise. 2021 · 2021
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Deep attention diffusion graph neural networks for text classification
Yonghao Liu, Renchu Guan, Fausto Giunchiglia, Yanchun Liang, and Xiaoyue Feng. 2021b · 2021
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Do you even need attention? A stack of feed-forward layers does surprisingly well on ImageNet
Luke Melas-Kyriazi. 2021 · 2021
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Deep learning-based text classification: A comprehensive review
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. 2021 · 2021
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HeteGCN: Heterogeneous graph convolutional networks for text classification
Rahul Ragesh, Sundararajan Sellamanickam, Arun Iyer, Ramakrishna Bairi, and Vijay Lingam. 2021 · 2021
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Masked language modeling and the distributional hypothesis: Order word matters pre-training for little
Koustuv Sinha, Robin Jia, Dieuwke Hupkes, Joelle Pineau, Adina Williams, and Douwe Kiela. 2021 · 2021
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MLP-Mixer: An all-MLP architecture for vision
Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. 2021 · 2021
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A comparison of deep-learning methods for analysing and predicting business processes
Ishwar Venugopal, Jessica Töllich, Michael Fairbank, and Ansgar Scherp. 2021 · 2021
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Graph neural networks for natural language processing: A survey
Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long. 2021 · 2021
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Rahul Yedida, Xueqi Yang, and Tim Menzies. 2021 · 2021
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Weakly-supervised text classification based on keyword graph
Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, and Shuigeng Zhou. 2021 · 2021
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