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Relation extraction (RE) is an indispensable information extraction task in several disciplines.
Dropout: A simple way to prevent neural networks from overfitting
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Identifying gene-disease associations using centrality on a literature mined gene-interaction network
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Exploiting constituent dependencies for tree kernel-based semantic relation extraction
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Design challenges and misconceptions in named entity recognition
Ratinov, Lev and Dan Roth. 2009 · 2009
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Joint entity and relation extraction using card-pyramid parsing
Kate, Rohit J and Raymond J Mooney. 2010 · 2010
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Jointly identifying entities and extracting relations in encyclopedia text via a graphical model approach
Yu, Xiaofeng and Wai Lam. 2010 · 2010
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A machine learning approach for identifying disease-treatment relations in short texts
Frunza, Oana, Diana Inkpen, and Thomas Tran. 2011 · 2011
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Automatic extraction of relations between medical concepts in clinical texts
Rink, Bryan, Sanda Harabagiu, and Kirk Roberts. 2011 · 2011
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Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports
Gurulingappa, Harsha, Abdul Mateen Rajput, Angus Roberts, Juliane Fluck, Martin Hofmann-Apitius, and Luca Toldo. 2012 · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, Tijmen and Geoffrey Hinton. 2012 · 2012
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Table filling multi-task recurrent neural network for joint entity and relation extraction
Gupta, Pankaj, Hinrich Schütze, and Bernt Andrassy. 2016 · 2016
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Joint models for extracting adverse drug events from biomedical text
Li, Fei, Yue Zhang, Meishan Zhang, and Donghong Ji. 2016 · 2016
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Dependency-based convolutional neural network for drug-drug interaction extraction
Liu, Shengyu, Kai Chen, Qingcai Chen, and Buzhou Tang. 2016 · 2016
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End-to-end relation extraction using LSTMs on sequences and tree structures
Miwa, Makoto and Mohit Bansal. 2016 · 2016
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Global normalization of convolutional neural networks for joint entity and relation classification
Adel, Heike and Hinrich Schütze. 2017 · 2017
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Distributional semantics resources for biomedical text processing
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Joint inference of entities, relations, and coreference
Singh, Sameer, Sebastian Riedel, Brian Martin, Jiaping Zheng, and Andrew McCallum. 2013 · 2013
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Incremental joint extraction of entity mentions and relations
Li, Qi and Heng Ji. 2014 · 2014
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Modeling joint entity and relation extraction with table representation
Miwa, Makoto and Yutaka Sasaki. 2014 · 2014
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Glove: Global vectors for word representation
Pennington, Jeffrey, Richard Socher, and Christopher Manning. 2014 · 2014
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Relation classification via convolutional deep neural network
Zeng, Daojian, Kang Liu, Siwei Lai, Guangyou Zhou, Jun Zhao, et al. 2014 · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Christian Szegedy. 2015 · 2015
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Katiyar, Arzoo and Claire Cardie. 2017 · 2017
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Extracting drug-drug interactions with word and character-level recurrent neural networks
Kavuluru, Ramakanth, Anthony Rios, and Tung Tran. 2017 · 2017
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A neural joint model for entity and relation extraction from biomedical text
Li, Fei, Meishan Zhang, Guohong Fu, and Donghong Ji. 2017 · 2017
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End-to-end relation extraction using neural networks and markov logic networks
Pawar, Sachin, Pushpak Bhattacharyya, and Girish Palshikar. 2017 · 2017
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Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text
Raj, Desh, SUNIL SAHU, and Ashish Anand. 2017 · 2017
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End-to-end neural relation extraction with global optimization
Zhang, Meishan, Yue Zhang, and Guohong Fu. 2017 · 2017
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Adversarial training for multi-context joint entity and relation extraction
Bekoulis, Giannis, Johannes Deleu, Thomas Demeester, and Chris Develder. 2018a · 2018
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Extracting relational facts by an end-to-end neural model with copy mechanism
Zeng, Xiangrong, Daojian Zeng, Shizhu He, Kang Liu, and Jun Zhao. 2018 · 2018
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Graph convolution over pruned dependency trees improves relation extraction
Zhang, Yuhao, Peng Qi, and Christopher D Manning. 2018 · 2018
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