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The DocRED dataset is one of the most popular and widely used benchmarks for document-level relation extraction (RE).
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Measuring nominal scale agreement among many raters
Joseph L Fleiss. 1971 · 1971
Earlier work this paper cites.
ACE 2005 multilingual training corpus
Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. 2006 · 2005
Earlier work this paper cites.
Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008 · 2008
Earlier work this paper cites.
The New York Times annotated corpus
Evan Sandhaus. 2008 · 2008
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Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009 · 2009
Earlier work this paper cites.
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.
Wikipedia entity expansion and attribute extraction from the web using semi-supervised learning
Lidong Bing, Wai Lam, and Tak-Lam Wong. 2013 · 2013
Earlier work this paper cites.
Relation extraction with matrix factorization and universal schemas
Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M. Marlin. 2013 · 2013
Earlier work this paper cites.
N-gram counts and language models from the common crawl
Christian Buck, Kenneth Heafield, and Bas Van Ooyen. 2014 · 2014
Earlier work this paper cites.
Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
Earlier work this paper cites.
Improving distant supervision for information extraction using label propagation through lists
Lidong Bing, Sneha Chaudhari, Richard Wang, and William Cohen. 2015 · 2015
Earlier work this paper cites.
Dbpedia – a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015 · 2015
Earlier work this paper cites.
Position-aware attention and supervised data improve slot filling
Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017 · 2017
Earlier work this paper cites.
Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
FewRel 2.0: Towards more challenging few-shot relation classification
Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2019 · 2019
Cited alongside, same era.
Effective attention modeling for neural relation extraction
Tapas Nayak and Hwee Tou Ng. 2019 · 2019
Cited alongside, same era.
Tackling long-tailed relations and uncommon entities in knowledge graph completion
Zihao Wang, Kwunping Lai, Piji Li, Lidong Bing, and Wai Lam. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
TACRED revisited: A thorough evaluation of the TACRED relation extraction task
Christoph Alt, Aleksandra Gabryszak, and Leonhard Hennig. 2020 · 2020
Cited alongside, same era.
Do not have enough data? deep learning to the rescue!
An end-to-end model for entity-level relation extraction using multi-instance learning
Markus Eberts and Adrian Ulges. 2021 · 2021
Later among the works it cites.
Manual evaluation matters: Reviewing test protocols of distantly supervised relation extraction
Tianyu Gao, Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2021 · 2021
Later among the works it cites.
Knowing false negatives: An adversarial training method for distantly supervised relation extraction
Kailong Hao, Botao Yu, and Wei Hu. 2021 · 2021
Later among the works it cites.
Empirical analysis of unlabeled entity problem in named entity recognition
Yangming Li, Lemao Liu, and Shuming Shi. 2021 · 2021
Later among the works it cites.
MulDA: A multilingual data augmentation framework for low-resource cross-lingual NER
Linlin Liu, Bosheng Ding, Lidong Bing, Shafiq Joty, Luo Si, and Chunyan Miao. 2021 · 2021
Later among the works it cites.
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Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, and Naama Zwerdling. 2020 · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
Cited alongside, same era.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
Cited alongside, same era.
More data, more relations, more context and more openness: A review and outlook for relation extraction
Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, and Maosong Sun. 2020 · 2020
Cited alongside, same era.
Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2020
Cited alongside, same era.
Reasoning with latent structure refinement for document-level relation extraction
Guoshun Nan, Zhijiang Guo, Ivan Sekulic, and Wei Lu. 2020 · 2020
Cited alongside, same era.
Effective modeling of encoder-decoder architecture for joint entity and relation extraction
Tapas Nayak and Hwee Tou Ng. 2020 · 2020
Cited alongside, same era.
Generating datasets with pretrained language models
Timo Schick and Hinrich Schütze. 2021 · 2021
Later among the works it cites.
Re-tacred: addressing shortcomings of the tacred dataset
George Stoica, Emmanouil Antonios Platanios, and Barnabás Póczos. 2021 · 2021
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Symbolic knowledge distillation: from general language models to commonsense models
Peter West, Chandra Bhagavatula, Jack Hessel, Jena D Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi. 2021 · 2021
Later among the works it cites.
Document-level relation extraction as semantic segmentation
Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, and Huajun Chen. 2021 · 2021
Later among the works it cites.
An improved baseline for sentence-level relation extraction
Wenxuan Zhou and Muhao Chen. 2021 · 2021
Later among the works it cites.
Document-level relation extraction with adaptive thresholding and localized context pooling
Wenxuan Zhou, Kevin Huang, Tengyu Ma, and Jing Huang. 2021 · 2021
Later among the works it cites.
RelationPrompt: leveraging prompts to generate synthetic data for zero-shot relation triplet extraction
Yew Ken Chia, Lidong Bing, Soujanya Poria, and Luo Si. 2022 · 2022
Closest in time.
Does recommend-revise produce reliable annotations? an analysis on missing instances in DocRED
Quzhe Huang, Shibo Hao, Yuan Ye, Shengqi Zhu, Yansong Feng, and Dongyan Zhao. 2022 · 2022
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Document-level relation extraction with adaptive focal loss and knowledge distillation
Qingyu Tan, Ruidan He, Lidong Bing, and Hwee Tou Ng. 2022 · 2022
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MELM: Data augmentation with masked entity language modeling for low-resource NER
Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, and Chunyan Miao. 2022 · 2022
Closest in time.