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Weak supervision has shown promising results in many natural language processing tasks, such as Named Entity Recognition (NER).
Roberta: A robustly optimized bert pretraining approach
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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Semi-supervised sequence modeling with cross-view training
Kevin Clark, Minh-Thang Luong, Christopher D. Manning, and Quoc Le. 2018 · 1925
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and P. S. Sastry. 2017 · 1925
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2018 · 1993
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky. 1995 · 1995
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001 · 2001
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Bianca Zadrozny and Charles Elkan. 2001 · 2001
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Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman. 2005 · 2005
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2020 · 2007
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Banner: an executable survey of advances in biomedical named entity recognition
Robert Leaman and Graciela Gonzalez. 2008 · 2008
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Coding chunkers as taggers: Io, bio, bmewo, and bmewo+
B Carpenter. 2009 · 2009
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Calibrated language model fine-tuning for in- and out-of-distribution data
Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, and C. Zhang. 2020 · 2010
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Generalized expectation criteria for semi-supervised learning with weakly labeled data
Gideon S Mann and Andrew McCallum. 2010 · 2010
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Adaptive self-training for few-shot neural sequence labeling
Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, and Ahmed Hassan Awadallah. 2020 · 2010
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Joint bilingual name tagging for parallel corpora
Qi Li, Haibo Li, Heng Ji, Wen Wang, Jing Zheng, and Fei Huang. 2012 · 2012
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Unsupervised aspect term extraction with B-LSTM & CRF using automatically labelled datasets
Athanasios Giannakopoulos, Claudiu Musat, Andreea Hossmann, and Michael Baeriswyl. 2017 · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola. 2017 · 2017
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Weakly-supervised neural text classification
Yu Meng, Jiaming Shen, Chao Zhang, and Jiawei Han. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Reinforcement-based denoising of distantly supervised NER with partial annotation
Farhad Nooralahzadeh, Jan Tore Lønning, and Lilja Øvrelid. 2019 · 2019
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Ncbi disease corpus: a resource for disease name recognition and concept normalization
Rezarta Islamaj Doğan, Robert Leaman, and Zhiyong Lu. 2014 · 2014
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Bidirectional lstm-crf models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2015 · 2015
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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. 2015 · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
Xuezhe Ma and Eduard Hovy. 2016 · 2016
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A neural network multi-task learning approach to biomedical named entity recognition
Gamal Crichton, Sampo Pyysalo, Billy Chiu, and Anna Korhonen. 2017 · 2017
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Swellshark: A generative model for biomedical named entity recognition without labeled data
Jason Fries, Sen Wu, Alex Ratner, and Christopher Ré. 2017 · 2017
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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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. 2020b · 2020
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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. 2020 · 2020
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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
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Self-training improves pre-training for natural language understanding
Jingfei Du, Edouard Grave, Beliz Gunel, Vishrav Chaudhary, Onur Celebi, Michael Auli, Veselin Stoyanov, and Alexis Conneau. 2021 · 2021
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Fine-tuning pre-trained language model with weak supervision: A contrastive-regularized self-training approach
Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, and Chao Zhang. 2021 · 2021
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Learning named entity tagger using domain-specific dictionary
Jingbo Shang, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren, and Jiawei Han. 2018 · 2064
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