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Deep residual learning (ResNet) is a new method for training very deep neural networks using identity map-ping for shortcut connections.
Kernel methods for relation extraction
Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. 2003 · 2003
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Subsequence kernels for relation extraction
Razvan Bunescu and Raymond J Mooney. 2005 · 2005
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Exploring various knowledge in relation extraction
Zhou GuoDong, Su Jian, Zhang Jie, and Zhang Min. 2005 · 2005
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Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009 · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio. 2010 · 2010
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Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010 · 2010
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Knowledge-based weak supervision for information extraction of overlapping relations
Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. 2011 · 2011
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Multi-instance multi-label learning for relation extraction
Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, and Christopher D Manning. 2012 · 2012
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 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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Attention-based convolutional neural network for semantic relation extraction
Yatian Shen and Xuanjing Huang. 2016 · 2016
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Very deep convolutional networks for natural language processing
Alexis Conneau, Holger Schwenk, Loïc Barrault, and Yann Lecun. 2017 · 2017
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Improved neural relation detection for knowledge base question answering
Mo Yu, Wenpeng Yin, Kazi Saidul Hasan, Cícero Nogueira dos Santos, Bing Xiang, and Bowen Zhou. 2017 · 2017
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