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Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step.
Kernel Methods for Relation Extraction
Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella · 2003
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A Shortest Path Dependency Kernel for Relation Extraction
Razvan C. Bunescu and Raymond J. Mooney · 2005
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Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky · 2009
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Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz · 2010
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Modeling relations and their mentions without labeled text
Sebastian Riedel, Limin Yao, and Andrew McCallum · 2010
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Utd: Classifying semantic relations by combining lexical and semantic resources
Bryan Rink and Sanda Harabagiu · 2010
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Word representations: A simple and general method for semi-supervised learning
Joseph Turian, Lev-Arie Ratinov, and Yoshua Bengio · 2010
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Identifying relations for open information extraction
Anthony Fader, Stephen Soderland, and Oren Etzioni · 2011
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Knowledge base population: Successful approaches and challenges
Heng Ji and Ralph Grishman · 2011
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Semantic compositionality through recursive matrix-vector spaces
Richard Socher, Brody Huval, Christopher D. Manning, and Andrew Y. Ng · 2012
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
Earlier work this paper cites.
Grounded compositional semantics for finding and describing images with sentences
Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, and Andrew Y. Ng · 2014
Cited alongside, same era.
Relation classification via convolutional deep neural network
Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Semantic relation classification via convolutional neural networks with simple negative sampling
Kun Xu, Yansong Feng, Songfang Huang, and Dongyan Zhao · 2015
Cited alongside, same era.
Classifying relations via long short term memory networks along shortest dependency paths
Yan Xu, Lili Mou, Ge Li, Yunchuan Chen, Hao Peng, and Zhi Jin · 2015
Cited alongside, same era.
Improved relation classification by deep recurrent neural networks with data augmentation
Yan Xu, Ran Jia, Lili Mou, Ge Li, Yunchuan Chen, Yangyang Lu, and Zhi Jin · 2016
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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
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Improved neural relation detection for knowledge base question answering
Mo Yu, Wenpeng Yin, Kazi Saidul Hasan, Cicero dos Santos, Bing Xiang, and Bowen Zhou · 2017
Later among the works it cites.
Position-aware attention and supervised data improve slot filling
Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning · 2017
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Hierarchical relation extraction with coarse-to-fine grained attention
Xu Han, Pengfei Yu, Zhiyuan Liu, Maosong Sun, and Peng Li · 2018
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Universal language model fine-tuning for text classification
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Distant supervision for relation extraction via piecewise convolutional neural networks
Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao · 2015
Cited alongside, same era.
Relation classification via recurrent neural network
Dongxu Zhang and Dong Wang · 2015
Cited alongside, same era.
Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
Cited alongside, same era.
Bidirectional recurrent convolutional neural network for relation classification
Rui Cai, Xiaodong Zhang, and Houfeng Wang · 2016
Cited alongside, same era.
Assessing the ability of lstms to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg · 2016
Cited alongside, same era.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
Cited alongside, same era.
Jeremy Howard and Sebastian Ruder · 2018
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Generating wikipedia by summarizing long sequences
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 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
Later among the works it cites.
Graph convolution over pruned dependency trees improves relation extraction
Yuhao Zhang, Peng Qi, and Christopher D. Manning · 2018
Later among the works it cites.