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Named entity recognition in real-world applications suffers from the diversity of entity types, the emergence of new entity types, and the lack of high-quality annotations.
Language models are few-shot learners
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Label semantics for few shot named entity recognition
Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, and Dan Roth. 2022a · 1971
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Lambda calculi with types
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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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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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Domain adaption of named entity recognition to support credit risk assessment
Julio Cesar Salinas Alvarado, Karin Verspoor, and Timothy Baldwin. 2015 · 2015
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
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Results of the WNUT2017 shared task on novel and emerging entity recognition
Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. 2017 · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
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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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Few-shot classification in named entity recognition task
Alexander Fritzler, Varvara Logacheva, and Maksim Kretov. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network
Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, and Ting Liu. 2020 · 2020
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Metaner: Named entity recognition with meta-learning
Jing Li, Shuo Shang, and Ling Shao. 2020b · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. 2020 · 2020
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Simple and effective few-shot named entity recognition with structured nearest neighbor learning
Yi Yang and Arzoo Katiyar. 2020 · 2020
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Ning Bian, Xianpei Han, Bo Chen, Hongyu Lin, Ben He, and Le Sun. 2021 · 2021
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Template-based named entity recognition using BART
Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. 2021 · 2021
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Meta-learning for few-shot named entity recognition
Cyprien de Lichy, Hadrien Glaude, and William Campbell. 2021 · 2021
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Few-shot named entity recognition: An empirical baseline study
Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, and Jiawei Han. 2021 · 2021
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Why can gpt learn in-context? language models secretly perform gradient descent as meta optimizers
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Zhifang Sui, and Furu Wei. 2022 · 2022
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CONTaiNER: Few-shot named entity recognition via contrastive learning
Sarkar Snigdha Sarathi Das, Arzoo Katiyar, Rebecca Passonneau, and Rui Zhang. 2022 · 2022
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Thinking about gpt-3 in-context learning for biomedical ie? think again
Bernal Jiménez Gutiérrez, Nikolas McNeal, Clay Washington, You Chen, Lang Li, Huan Sun, and Yu Su. 2022 · 2022
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Few-shot named entity recognition with entity-level prototypical network enhanced by dispersedly distributed prototypes
Bin Ji, Shasha Li, Shaoduo Gan, Jie Yu, Jun Ma, Huijun Liu, and Jing Yang. 2022 · 2022
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Good examples make a faster learner: Simple demonstration-based learning for low-resource NER
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Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
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Learning from miscellaneous other-class words for few-shot named entity recognition
Meihan Tong, Shuai Wang, Bin Xu, Yixin Cao, Minghui Liu, Lei Hou, and Juanzi Li. 2021 · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
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Learning from language description: Low-shot named entity recognition via decomposed framework
Yaqing Wang, Haoda Chu, Chao Zhang, and Jing Gao. 2021a · 2021
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A unified generative framework for various NER subtasks
Hang Yan, Tao Gui, Junqi Dai, Qipeng Guo, Zheng Zhang, and Xipeng Qiu. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. 2022 · 2022
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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, and Xiang Ren. 2022 · 2022
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Qaner: Prompting question answering models for few-shot named entity recognition
Andy T Liu, Wei Xiao, Henghui Zhu, Dejiao Zhang, Shang-Wen Li, and Andrew Arnold. 2022 · 2022
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Template-free prompt tuning for few-shot NER
Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang, and Xuanjing Huang. 2022b · 2022
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Decomposed meta-learning for few-shot named entity recognition
Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, and Chin-Yew Lin. 2022c · 2022
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MetaICL: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022a · 2022
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
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Transformers learn in-context by gradient descent
Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov. 2022 · 2022
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An enhanced span-based decomposition method for few-shot sequence labeling
Peiyi Wang, Runxin Xu, Tianyu Liu, Qingyu Zhou, Yunbo Cao, Baobao Chang, and Zhifang Sui. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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SEE-few: Seed, expand and entail for few-shot named entity recognition
Zeng Yang, Linhai Zhang, and Deyu Zhou. 2022 · 2022
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