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Recent studies in deep learning have shown significant progress in named entity recognition (NER).
Low-resource name tagging learned with weakly labeled data
Yixin Cao, Zikun Hu, Tat-Seng Chua, Zhiyuan Liu, and Heng Ji. 2019b · 1908
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A stochastic approximation method
Herbert Robbins and Sutton Monro. 1951 · 1951
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Nltk: The natural language toolkit
Edward Loper and Steven Bird. 2002 · 2002
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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An introduction to conditional random fields for relational learning
Charles Sutton and Andrew McCallum. 2006 · 2006
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Learning extractors from unlabeled text using relevant databases
Kedar Bellare and Andrew McCallum. 2007 · 2007
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Named entity recognition in wikipedia
Dominic Balasuriya, Nicky Ringland, Joel Nothman, Tara Murphy, and James R Curran. 2009 · 2009
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Design challenges and misconceptions in named entity recognition
Lev Ratinov and Dan Roth. 2009 · 2009
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
Cited alongside, same era.
Multimedia lab@ acl wnut ner shared task: Named entity recognition for twitter microposts using distributed word representations
Fréderic Godin, Baptist Vandersmissen, Wesley De Neve, and Rik Van de Walle. 2015 · 2015
Cited alongside, same era.
Bidirectional lstm-crf models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2015 · 2015
Cited alongside, same era.
Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
Cited alongside, same era.
End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard Hovy. 2016 · 2016
Cited alongside, same era.
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. 2018 · 2018
Later among the works it cites.
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning
Yaosheng Yang, Wenliang Chen, Zhenghua Li, Zhengqiu He, and Min Zhang. 2018 · 2018
Later among the works it cites.
Unsupervised label noise modeling and loss correction
Eric Arazo, Diego Ortego, Paul Albert, Noel O’Connor, and Kevin Mcguinness. 2019 · 2019
Later among the works it cites.
Low-resource name tagging learned with weakly labeled data
Yixin Cao, Zikun Hu, Tat-seng Chua, Zhiyuan Liu, and Heng Ji. 2019a · 2019
Later among the works it cites.
Better modeling of incomplete annotations for named entity recognition
Zhanming Jie, Pengjun Xie, Wei Lu, Ruixue Ding, and Linlin Li. 2019 · 2019
Later among the works it cites.
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Tetsuro Sasada, Shinsuke Mori, Tatsuya Kawahara, and Yoko Yamakata. 2016 · 2016
Cited alongside, same era.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzundefinedbski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien. 2017 · 2017
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
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. 2018b · 2018
Cited alongside, same era.
Training a neural network in a low-resource setting on automatically annotated noisy data
Michael A. Hedderich and Dietrich Klakow. 2018 · 2018
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018a
Cited in the paper.
Stephen Mayhew, Snigdha Chaturvedi, Chen-Tse Tsai, and Dan Roth. 2019 · 2019
Later among the works it cites.
Distantly supervised named entity recognition using positive-unlabeled learning
Minlong Peng, Xiaoyu Xing, Qi Zhang, Jinlan Fu, and Xuan-Jing Huang. 2019 · 2019
Later among the works it cites.
Bond: Bert-assisted open-domain named entity recognition with distant supervision
Chen Liang, Yue Yu, Haoming Jiang, Siawpeng Er, Ruijia Wang, Tuo Zhao, and Chao Zhang. 2020 · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Later among the works it cites.
Learning named entity tagger using domain-specific dictionary
Jingbo Shang, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren, and Jiawei Han. 2018 · 2064
Closest in time.