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Relation extraction (RE) aims to identify the semantic relations between named entities in text.
Fine-tune Bert for DocRED with two-step process
Hong Wang, Christfried Focke, Rob Sylvester, Nilesh Mishra, and William Wang. 2019 · 1909
Earlier work this paper cites.
CRYSTAL: Inducing a conceptual dictionary
Stephen Soderland, David Fisher, Jonathan Aseltine, and Wendy Lehnert. 1995 · 1995
Earlier work this paper cites.
Dependency tree kernels for relation extraction
Aron Culotta and Jeffrey Sorensen. 2004 · 2004
Earlier work this paper cites.
Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations
Nanda Kambhatla. 2004 · 2004
Earlier work this paper cites.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba. 2015 · 2015
Earlier work this paper cites.
Distant supervision for relation extraction via piecewise convolutional neural networks
Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. 2015 · 2015
Earlier work this paper cites.
Bidirectional long short-term memory networks for relation classification
Shu Zhang, Dequan Zheng, Xinchen Hu, and Ming Yang. 2015 · 2015
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016 · 2016
Earlier work this paper cites.
Bidirectional recurrent convolutional neural network for relation classification
Rui Cai, Xiaodong Zhang, and Houfeng Wang. 2016 · 2016
Earlier work this paper cites.
How to train good word embeddings for biomedical NLP
Billy Chiu, Gamal Crichton, Anna Korhonen, and Sampo Pyysalo. 2016 · 2016
Earlier work this paper cites.
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.
Neural relation extraction with selective attention over instances
Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. 2016 · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Chemical-induced disease relation extraction via convolutional neural network
Jinghang Gu, Fuqing Sun, Longhua Qian, and Guodong Zhou. 2017 · 2017
Cited alongside, same era.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Cross-sentence n 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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Context-aware representations for knowledge base relation extraction
Daniil Sorokin and Iryna Gurevych. 2017 · 2017
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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 · 2017
Connecting the dots: Document-level neural relation extraction with edge-oriented graphs
Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2019 · 2019
Later among the works it cites.
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Neural relation extraction within and across sentence boundaries
Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, and Thomas Runkler. 2019 · 2019
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BioBERT: A pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 2019
Later among the works it cites.
Neural machine reading comprehension: Methods and trends
Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, and Weiming Zhang. 2019 · 2019
Later among the works it cites.
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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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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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QA4IE: A question answering based framework for information extraction
Lin Qiu, Hao Zhou, Yanru Qu, Weinan Zhang, Suoheng Li, Shu Rong, Dongyu Ru, Lihua Qian, Kewei Tu, and Yong Yu. 2018 · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 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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A fine-grained and noise-aware method for neural relation extraction
Jianfeng Qu, Wen Hua, Dantong Ouyang, Xiaofang Zhou, and Ximing Li. 2019 · 2019
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Inter-sentence relation extraction with document-level graph convolutional neural network
Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 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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DocRED: A large-scale document-level relation extraction dataset
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun. 2019 · 2019
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Long-tail relation extraction via knowledge graph embeddings and graph convolution networks
Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, and Huajun Chen. 2019 · 2019
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ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
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Reasoning with latent structure refinement for document-level relation extraction
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Coreferential reasoning learning for language representation
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