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Document-level relation extraction is a complex human process that requires logical inference to extract relationships between named entities in text.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Bidirectional recurrent neural networks
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Shortest-path kernels on graphs
Karsten M Borgwardt and Hans-Peter Kriegel. 2005 · 2005
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Open information extraction from the web
Michele Banko, Michael J Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. 2007 · 2007
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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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Modeling relations and their mentions without labeled text
Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010 · 2010
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Entity linking with a knowledge base: Issues, techniques, and solutions
Wei Shen, Jianyong Wang, and Jiawei Han. 2014 · 2014
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
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Relation extraction: Perspective from convolutional neural networks
Thien Huu Nguyen and Ralph Grishman. 2015 · 2015
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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 · 2015
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Distant supervision for relation extraction via piecewise convolutional neural networks
Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. 2015 · 2015
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How to train good word embeddings for biomedical nlp
Billy Chiu, Gamal Crichton, Anna Korhonen, and Sampo Pyysalo. 2016 · 2016
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Chemical-induced disease relation extraction with various linguistic features
Jinghang Gu, Longhua Qian, and Guodong Zhou. 2016 · 2016
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Relation extraction with multi-instance multi-label convolutional neural networks
Xiaotian Jiang, Quan Wang, Peng Li, and Bin Wang. 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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End-to-end relation extraction using LSTMs on sequences and tree structures
Makoto Miwa and Mohit Bansal. 2016 · 2016
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Improving chemical disease relation extraction with rich features and weakly labeled data
Yifan Peng, Chih-Hsuan Wei, and Zhiyong Lu. 2016 · 2016
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Disgenet: a comprehensive platform integrating information on human disease-associated genes and variants
Chemical-induced disease extraction via recurrent piecewise convolutional neural networks
Haodi Li, Ming Yang, Qingcai Chen, Buzhou Tang, Xiaolong Wang, and Jun Yan. 2018 · 2018
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Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings
Dat Quoc Nguyen and Karin Verspoor. 2018 · 2018
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Exploiting graph kernels for high performance biomedical relation extraction
Nagesh C Panyam, Karin Verspoor, Trevor Cohn, and Kotagiri Ramamohanarao. 2018 · 2018
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N-ary relation extraction using graph-state lstm
Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018 · 2018
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Reside: Improving distantly-supervised neural relation extraction using side information
Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya, and Partha Talukdar. 2018 · 2018
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Janet Piñero, Àlex Bravo, Núria Queralt-Rosinach, Alba Gutiérrez-Sacristán, Jordi Deu-Pons, Emilio Centeno, Javier García-García, Ferran Sanz, and Laura I Furlong. 2016 · 2016
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Relation classification via multi-level attention CNNs
Linlin Wang, Zhu Cao, Gerard de Melo, and Zhiyuan Liu. 2016 · 2016
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Exploiting syntactic and semantics information for chemical–disease relation extraction
Huiwei Zhou, Huijie Deng, Long Chen, Yunlong Yang, Chen Jia, and Degen Huang. 2016 · 2016
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Chemical-induced disease relation extraction via convolutional neural network
Jinghang Gu, Fuqing Sun, Longhua Qian, and Guodong Zhou. 2017 · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Cross-sentence n-ary relation extraction with graph lstms
Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. 2017 · 2017
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Distant supervision for relation extraction beyond the sentence boundary
Chris Quirk and Hoifung Poon. 2017 · 2017
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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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Graph convolution over pruned dependency trees improves relation extraction
Yuhao Zhang, Peng Qi, and Christopher D Manning. 2018 · 2018
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An effective neural model extracting document level chemical-induced disease relations from biomedical literature
Wei Zheng, Hongfei Lin, Zhiheng Li, Xiaoxia Liu, Zhengguang Li, Bo Xu, Yijia Zhang, Zhihao Yang, and Jian Wang. 2018 · 2018
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Distant supervision for relation extraction with hierarchical selective attention
Peng Zhou, Jiaming Xu, Zhenyu Qi, Hongyun Bao, Zhineng Chen, and Bo Xu. 2018 · 2018
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Neural relation extraction within and across sentence boundaries
Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, and Thomas Runkler. 2019 · 2019
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Document-level n-ary relation extraction with multiscale representation learning
Robin Jia, Cliff Wong, and Hoifung Poon. 2019 · 2019
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A general framework for information extraction using dynamic span graphs
Yi Luan, Dave Wadden, Luheng He, Amy Shah, Mari Ostendorf, and Hannaneh Hajishirzi. 2019 · 2019
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Relation extraction using explicit context conditioning
Gaurav Singh and Parminder Bhatia. 2019 · 2019
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Renet: A deep learning approach for extracting gene-disease associations from literature
Ye Wu, Ruibang Luo, Henry CM Leung, Hing-Fung Ting, and Tak-Wah Lam. 2019 · 2019
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