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Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues.
Biobert: pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 1901
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Simple bert models for relation extraction and semantic role labeling
Peng Shi and Jimmy Lin. 2019 · 1904
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Matching the blanks: Distributional similarity for relation learning
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019 · 1906
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Lamal: Language modeling is all you need for lifelong language learning
Fan-Keng Sun, Cheng-Hao Ho, and Hung-Yi Lee. 2019a · 1909
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Neural network classification and prior class probabilities
Steve Lawrence, Ian Burns, Andrew Back, Ah Chung Tsoi, and C Lee Giles. 1998 · 1998
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The class imbalance problem: A systematic study
Nathalie Japkowicz and Shaju Stephen. 2002 · 2002
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Rcv1: A new benchmark collection for text categorization research
David D Lewis, Yiming Yang, Tony G Rose, and Fan Li. 2004 · 2004
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Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhi-Hua Zhou and Xu-Ying Liu. 2005 · 2005
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Open information extraction from the web
Michele Banko, Michael J Cafarella, Stephen Soderland, Matthew Broadhead, and Oren Etzioni. 2007 · 2007
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Roughly balanced bagging for imbalanced data
Shohei Hido, Hisashi Kashima, and Yutaka Takahashi. 2009 · 2009
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Exploiting probabilistic topic models to improve text categorization under class imbalance
Enhong Chen, Yanggang Lin, Hui Xiong, Qiming Luo, and Haiping Ma. 2011 · 2011
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Identifying relations for open information extraction
Anthony Fader, Stephen Soderland, and Oren Etzioni. 2011 · 2011
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Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (ddiextraction 2013)
Isabel Segura Bedmar, Paloma Martínez, and María Herrero Zazo. 2013 · 2013
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Relation classification via convolutional deep neural network
Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014 · 2014
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Adaptive data augmentation for image classification
Overview of the biocreative vi chemical-protein interaction track
Martin Krallinger, Obdulia Rabal, Saber A Akhondi, et al. 2017 · 2017
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Fixing weight decay regularization in adam
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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The effectiveness of data augmentation in image classification using deep learning
Jason Wang and Luis Perez. 2017 · 2017
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Adversarial training for relation extraction
Yi Wu, David Bamman, and Stuart Russell. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Alhussein Fawzi, Horst Samulowitz, Deepak Turaga, and Pascal Frossard. 2016 · 2016
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Biocreative v cdr task corpus: a resource for chemical disease relation extraction
Jiao Li, Yueping Sun, Robin J Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Thomas C Wiegers, and Zhiyong Lu. 2016 · 2016
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Neural relation extraction with selective attention over instances
Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. 2016 · 2016
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Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré. 2016 · 2016
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Understanding data augmentation for classification: when to warp?
Sebastien C Wong, Adam Gatt, Victor Stamatescu, and Mark D McDonnell. 2016 · 2016
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Data augmentation for visual question answering
Kushal Kafle, Mohammed Yousefhussien, and Christopher Kanan. 2017 · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018a
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification
Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, and Hayit Greenspan. 2018 · 2018
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Extracting chemical–protein relations with ensembles of svm and deep learning models
Yifan Peng, Anthony Rios, Ramakanth Kavuluru, and Zhiyong Lu. 2018 · 2018
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Simultaneously self-attending to all mentions for full-abstract biological relation extraction
Patrick Verga, Emma Strubell, and Andrew McCallum. 2018 · 2018
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A bert-based universal model for both within-and cross-sentence clinical temporal relation extraction
Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2019 · 2019
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Deep bidirectional transformers for relation extraction without supervision
Yannis Papanikolaou, Ian Roberts, and Andrea Pierleoni. 2019 · 2019
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