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Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document.
Improving relation extraction by pre-trained language representations
Christoph Alt, Marc Hübner, and Leonhard Hennig. 2019 · 1906
Earlier work this paper cites.
What does bert look at? an analysis of bert’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 1906
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2019 · 1907
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Kernel methods for relation extraction
Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. 2003 · 2003
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A shortest path dependency kernel for relation extraction
Razvan Bunescu and Raymond Mooney. 2005 · 2005
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Improving relation classification by entity pair graph
Yi Zhao, Huaiyu Wan, Jianwei Gao, and Youfang Lin. 2019 · 2008
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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 · 2010
Earlier work this paper cites.
Relation classification via convolutional deep neural network
Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014 · 2014
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Bidirectional recurrent convolutional neural network for relation classification
Rui Cai, Xiaodong Zhang, and Houfeng Wang. 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
Earlier work this paper cites.
Relation classification via multi-level attention CNNs
Linlin Wang, Zhu Cao, Gerard de Melo, and Zhiyuan Liu. 2016 · 2016
Earlier work this paper cites.
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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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 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 · 2017
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Span-based joint entity and relation extraction with transformer pre-training
Markus Eberts and Adrian Ulges. 2019 · 2019
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Attention guided graph convolutional networks for relation extraction
Zhijiang Guo, Yan Zhang, and Wei Lu. 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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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Neural relation extraction for knowledge base enrichment
Bayu Distiawan Trisedya, Gerhard Weikum, Jianzhong Qi, and Rui Zhang. 2019 · 2019
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Entity, relation, and event extraction with contextualized span representations
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Position-aware attention and supervised data improve slot filling
Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017 · 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 · 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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Augmenting end-to-end dialog systems with commonsense knowledge
Tom Young, Erik Cambria Cambria, Iti Chaturvedi, Minlie Huang, Hao Zhou, and Subham Biswas. 2018 · 2018
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Matching the blanks: Distributional similarity for relation learning
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019 · 2019
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Connecting the dots: Document-level neural relation extraction with edge-oriented graphs
Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019a
Cited in the paper.
David Wadden, Ulme Wennberg, Yi Luan, and Hannaneh Hajishirzi. 2019 · 2019
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Fine-tune bert for docred with two-step process
Hong Wang, Christfried Focke, Rob Sylvester, Nilesh Mishra, and William Wang. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Reasoning with latent structure refinement for document-level relation extraction
Guoshun Nan, Zhijiang Guo, Ivan Sekulić, and Wei Lu. 2020 · 2020
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Hin: Hierarchical inference network for document-level relation extraction
Hengzhu Tang, Yanan Cao, Zhenyu Zhang, Jiangxia Cao, Fang Fang, Shi Wang, and Pengfei Yin. 2020 · 2020
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Ro{bert}a: A robustly optimized {bert} pretraining approach
Naman Goyal Yinhan Liu, Myle Ott. 2020 · 2020
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