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Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments.
TENER: adapting transformer encoder for named entity recognition
Hang Yan, Bocao Deng, Xiaonan Li, and Xipeng Qiu. 2019 · 1911
Earlier work this paper cites.
Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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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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Practical variational inference for neural networks
Alex Graves. 2011 · 2011
Earlier work this paper cites.
OntoNotes Release 5.0
Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, and Ann Houston. 2013 · 2013
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling. 2015 · 2015
Earlier work this paper cites.
Joint mention extraction and classification with mention hypergraphs
Wei Lu and Dan Roth. 2015 · 2015
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
Earlier work this paper cites.
Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
Earlier work this paper cites.
Deep variational information bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. 2017 · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
Earlier work this paper cites.
Contextual string embeddings for sequence labeling
Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018 · 2018
Cited alongside, same era.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin. 2018 · 2018
Cited alongside, same era.
Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales. 2018 · 2018
Cited alongside, same era.
Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance M. Kaplan, and Melih Kandemir. 2018 · 2018
Cited alongside, same era.
Adaptive co-attention network for named entity recognition in tweets
Qi Zhang, Jinlan Fu, Xiaoyu Liu, and Xuanjing Huang. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
SpanNER: Named entity re-/recognition as span prediction
Jinlan Fu, Xuanjing Huang, and Pengfei Liu. 2021 · 2021
Later among the works it cites.
Trusted multi-view classification
Zongbo Han, Changqing Zhang, Huazhu Fu, and Joey Tianyi Zhou. 2021 · 2021
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Uncertainty-aware reliable text classification
Yibo Hu and Latifur Khan. 2021 · 2021
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Evaluating robustness of predictive uncertainty estimation: Are dirichlet-based models reliable?
Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, and Stephan Günnemann. 2021 · 2021
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Misclassification risk and uncertainty quantification in deep classifiers
Murat Sensoy, Maryam Saleki, Simon Julier, Reyhan Aydogan, and John Reid. 2021 · 2021
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TextFlint: Unified multilingual robustness evaluation toolkit for natural language processing
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Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Cited alongside, same era.
Deep evidential regression
Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus. 2020 · 2020
Cited alongside, same era.
Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
Bertrand Charpentier, Daniel Zügner, and Stephan Günnemann. 2020 · 2020
Cited alongside, same era.
An analysis of simple data augmentation for named entity recognition
Xiang Dai and Heike Adel. 2020 · 2020
Cited alongside, same era.
Robust Backed-off Estimation of Out-of-Vocabulary Embeddings
Nobukazu Fukuda, Naoki Yoshinaga, and Masaru Kitsuregawa. 2020 · 2020
Cited alongside, same era.
LUKE: Deep contextualized entity representations with entity-aware self-attention
Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, and Yuji Matsumoto. 2020 · 2020
Cited alongside, same era.
Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, and Zexiong and Pang. 2021 · 2021
Later among the works it cites.
A unified generative framework for various NER subtasks
Hang Yan, Tao Gui, Junqi Dai, Qipeng Guo, Zheng Zhang, and Xipeng Qiu. 2021 · 2021
Later among the works it cites.
Unified named entity recognition as word-word relation classification
Jingye Li, Hao Fei, Jiang Liu, Shengqiong Wu, Meishan Zhang, Chong Teng, Donghong Ji, and Fei Li. 2022 · 2022
Later among the works it cites.
An impartial take to the cnn vs transformer robustness contest
Francesco Pinto, Philip HS Torr, and Puneet K Dokania. 2022 · 2022
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MINER: Improving out-of-vocabulary named entity recognition from an information theoretic perspective
Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, and Xuanjing Huang. 2022 · 2022
Later among the works it cites.
De-bias for generative extraction in unified NER task
Shuai Zhang, Yongliang Shen, Zeqi Tan, Yiquan Wu, and Weiming Lu. 2022 · 2022
Later among the works it cites.
Boundary smoothing for named entity recognition
Enwei Zhu and Jinpeng Li. 2022 · 2022
Later among the works it cites.