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Active learning, a widely adopted technique for enhancing machine learning models in text and image classification tasks with limited annotation resources, has received relatively little attention in the domain of Named Entity Recognition (NER).
Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2019 · 1906
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Learning from imbalanced data sets with weighted cross-entropy function
Yuri Sousa Aurelio, Gustavo Matheus De Almeida, Cristiano Leite de Castro, and Antonio Padua Braga. 2019 · 1949
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Cross-lingual name tagging and linking for 282 languages
Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, and Heng Ji. 2017 · 1958
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Active hidden markov models for information extraction
Tobias Scheffer, Christian Decomain, and Stefan Wrobel. 2001 · 2001
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Sang and Fien De Meulder. 2003 · 2003
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Confidence-based active learning
Mingkun Li and Ishwar K Sethi. 2006 · 2006
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Uncertainty-aware active learning for optimal bayesian classifier
Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, and Xiaoning Qian. 2021 · 2006
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven. 2008 · 2008
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Seqmix: Augmenting active sequence labeling via sequence mixup
Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020 · 2010
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. 2011 · 2011
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Active learning with subsequence sampling strategy for sequence labeling tasks
Dittaya Wanvarie, Hiroya Takamura, and Manabu Okumura. 2011 · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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An experimental comparison of active learning strategies for partially labeled sequences
Diego Marcheggiani and Thierry Artieres. 2014 · 2014
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
Cited alongside, same era.
Assessing the state of the art in biomedical relation extraction: overview of the biocreative v chemical-disease relation (cdr) task
Chih-Hsuan Wei, Yifan Peng, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Jiao Li, Thomas C Wiegers, and Zhiyong Lu. 2016 · 2016
Cited alongside, same era.
Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017 · 2017
Cited alongside, same era.
Cost-sensitive learning of deep feature representations from imbalanced data
Salman H Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A Sohel, and Roberto Togneri. 2017 · 2017
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese. 2017 · 2017
Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning
Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin. 2020 · 2020
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Deep representation learning on long-tailed data: A learnable embedding augmentation perspective
Jialun Liu, Yifan Sun, Chuchu Han, Zhaopeng Dou, and Wenhui Li. 2020 · 2020
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Equalization loss for long-tailed object recognition
Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan. 2020 · 2020
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Subsequence based deep active learning for named entity recognition
Puria Radmard, Yassir Fathullah, and Aldo Lipani. 2021 · 2021
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Active learning for sequence tagging with deep pre-trained models and bayesian uncertainty estimates
Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V Dylov, and Alexander Panchenko. 2021 · 2021
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Cited alongside, same era.
Deep active learning for named entity recognition
Yanyao Shen, Hyokun Yun, Zachary C Lipton, Yakov Kronrod, and Animashree Anandkumar. 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.
Gradient harmonized single-stage detector
Buyu Li, Yu Liu, and Xiaogang Wang. 2018 · 2018
Cited alongside, same era.
The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. 2018 · 2018
Cited alongside, same era.
Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. 2019 · 2019
Cited alongside, same era.
Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. 2019 · 2019
Cited alongside, same era.
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal. 2019 · 2019
Cited alongside, same era.
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Diversity enhanced active learning with strictly proper scoring rules
Wei Tan, Lan Du, and Wray Buntine. 2021 · 2021
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Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning
Chen Wei, Kihyuk Sohn, Clayton Mellina, Alan Yuille, and Fan Yang. 2021 · 2021
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Active learning on a budget: Opposite strategies suit high and low budgets
Guy Hacohen, Avihu Dekel, and Daphna Weinshall. 2022 · 2022
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Smoothed adaptive weighting for imbalanced semi-supervised learning: Improve reliability against unknown distribution data
Zhengfeng Lai, Chao Wang, Henrry Gunawan, Sen-Ching S Cheung, and Chen-Nee Chuah. 2022 · 2022
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Ltp: a new active learning strategy for crf-based named entity recognition
Mingyi Liu, Zhiying Tu, Tong Zhang, Tonghua Su, Xiaofei Xu, and Zhongjie Wang. 2022 · 2022
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Making look-ahead active learning strategies feasible with neural tangent kernels
Mohamad Amin Mohamadi, Wonho Bae, and Danica J Sutherland. 2022 · 2022
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Deep active learning by leveraging training dynamics
Haonan Wang, Wei Huang, Ziwei Wu, Hanghang Tong, Andrew J Margenot, and Jingrui He. 2022 · 2022
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Easal: entity-aware subsequence-based active learning for named entity recognition
Yang Liu, Jinpeng Hu, Zhihong Chen, Xiang Wan, and Tsung-Hui Chang. 2023 · 2023
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