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Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples.
The viterbi algorithm
G David Forney. 1973 · 1973
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001 · 2001
Earlier work this paper cites.
Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang. 2002 · 2002
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Towards robust linguistic analysis using OntoNotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013 · 2013
Earlier work this paper cites.
Named entity recognition with bidirectional LSTM-CNNs
Jason P.C. Chiu and Eric Nichols. 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.
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
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Earlier work this paper cites.
Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy P. Lillicrap. 2016 · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra. 2016 · 2016
Earlier work this paper cites.
Results of the WNUT2017 shared task on novel and emerging entity recognition
Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. 2017 · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Semi-supervised sequence tagging with bidirectional language models
Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 2017 · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
Earlier work this paper cites.
The gum corpus: Creating multilayer resources in the classroom
Amir Zeldes. 2017 · 2017
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. 2018 · 2018
Cited alongside, same era.
Meta relational learning for few-shot link prediction in knowledge graphs
Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen, and Huajun Chen. 2019 · 2019
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
Cited alongside, same era.
Few-shot classification in named entity recognition task
Alexander Fritzler, Varvara Logacheva, and Maksim Kretov. 2019 · 2019
Cited alongside, same era.
Induction networks for few-shot text classification
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.
Enhanced meta-learning for cross-lingual named entity recognition with minimal resources
Qianhui Wu, Zijia Lin, Guoxin Wang, Hui Chen, Börje F. Karlsson, Biqing Huang, and Chin-Yew Lin. 2020 · 2020
Later among the works it cites.
Simple and effective few-shot named entity recognition with structured nearest neighbor learning
Yi Yang and Arzoo Katiyar. 2020 · 2020
Later among the works it cites.
Template-based named entity recognition using BART
Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. 2021 · 2021
Later among the works it cites.
Container: Few-shot named entity recognition via contrastive learning
Sarkar Snigdha Sarathi Das, Arzoo Katiyar, Rebecca J Passonneau, and Rui Zhang. 2021 · 2021
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Ruiying Geng, Binhua Li, Yongbin Li, Xiaodan Zhu, Ping Jian, and Jian Sun. 2019 · 2019
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Towards improving neural named entity recognition with gazetteers
Tianyu Liu, Jin-Ge Yao, and Chin-Yew Lin. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Tapnet: Neural network augmented with task-adaptive projection for few-shot learning
Sung Whan Yoon, Jun Seo, and Jaekyun Moon. 2019 · 2019
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Augmented natural language for generative sequence labeling
Ben Athiwaratkun, Cicero Nogueira dos Santos, Jason Krone, and Bing Xiang. 2020 · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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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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Meta-learning for few-shot named entity recognition
Cyprien de Lichy, Hadrien Glaude, and William Campbell. 2021 · 2021
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Few-NERD: A few-shot named entity recognition dataset
Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, and Zhiyuan Liu. 2021 · 2021
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SpanNER: Named entity re-/recognition as span prediction
Jinlan Fu, Xuanjing Huang, and Pengfei Liu. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 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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Learning from language description: Low-shot named entity recognition via decomposed framework
Yaqing Wang, Haoda Chu, Chao Zhang, and Jing Gao. 2021b · 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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Few-shot intent classification and slot filling with retrieved examples
Dian Yu, Luheng He, Yuan Zhang, Xinya Du, Panupong Pasupat, and Qi Li. 2021 · 2021
Later among the works it cites.