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Self-augmentation has received increasing research interest recently to improve named entity recognition (NER) performance in low-resource scenarios.
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Named entity recognition with long short-term memory
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
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Instance weighting for domain adaptation in NLP
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Ontonotes release 4.0
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Towards robust linguistic analysis using ontonotes
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Germeval 2014 named entity recognition shared task: Companion paper
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 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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Named entity recognition for chinese social media with jointly trained embeddings
Nanyun Peng and Mark Dredze. 2015 · 2015
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Named entity recognition with bidirectional lstm-cnns
Jason P. C. Chiu and Eric Nichols. 2016 · 2016
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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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End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard H. Hovy. 2016 · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
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Instance weighting for neural machine translation domain adaptation
Rui Wang, Masao Utiyama, Lemao Liu, Kehai Chen, and Eiichiro Sumita. 2017 · 2017
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Contextual augmentation: Data augmentation by words with paradigmatic relations
Sosuke Kobayashi. 2018 · 2018
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Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. 2018 · 2018
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Instance weighting for domain adaptation via trading off sample selection bias and variance
Rui Xia, Zhenchun Pan, and Feng Xu. 2018 · 2018
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Hybrid semi-markov CRF for neural sequence labeling
Zhi-Xiu Ye and Zhen-Hua Ling. 2018 · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018
DAGA: Data augmentation with a generation approach for low-resource tagging tasks
Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, and Chunyan Miao. 2020 · 2020
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SLK-NER: exploiting second-order lexicon knowledge for chinese NER
Dou Hu and Lingwei Wei. 2020 · 2020
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FLAT: chinese NER using flat-lattice transformer
Xiaonan Li, Hang Yan, Xipeng Qiu, and Xuanjing Huang. 2020a · 2020
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A unified MRC framework for named entity recognition
Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, and Jiwei Li. 2020b · 2020
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Simplify the usage of lexicon in chinese NER
Ruotian Ma, Minlong Peng, Qi Zhang, Zhongyu Wei, and Xuanjing Huang. 2020 · 2020
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Porous lattice transformer encoder for Chinese NER
Xue Mengge, Bowen Yu, Tingwen Liu, Yue Zhang, Erli Meng, and Bin Wang. 2020 · 2020
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Cited alongside, same era.
Chinese NER using lattice LSTM
Yue Zhang and Jie Yang. 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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Merge and label: A novel neural network architecture for nested NER
Joseph Fisher and Andreas Vlachos. 2019 · 2019
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Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation
Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, and Partha P. Talukdar. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Glyce: Glyph-vectors for chinese character representations
Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, and Jiwei Li. 2019 · 2019
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Improving named entity recognition with attentive ensemble of syntactic information
Yuyang Nie, Yuanhe Tian, Yan Song, Xiang Ao, and Xiang Wan. 2020a · 2020
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Named entity recognition for social media texts with semantic augmentation
Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, and Bo Dai. 2020b · 2020
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Mixup-transformer: Dynamic data augmentation for NLP tasks
Lichao Sun, Congying Xia, Wenpeng Yin, Tingting Liang, Philip S. Yu, and Lifang He. 2020 · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, and Quoc Le. 2020 · 2020
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FGN: fusion glyph network for chinese named entity recognition
Zhenyu Xuan, Rui Bao, and Shengyi Jiang. 2020 · 2020
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Wikipedia2vec: An efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia
Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, and Yuji Matsumoto. 2020 · 2020
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Named entity recognition as dependency parsing
Juntao Yu, Bernd Bohnet, and Massimo Poesio. 2020 · 2020
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Counterfactual generator: A weakly-supervised method for named entity recognition
Xiangji Zeng, Yunliang Li, Yuchen Zhai, and Yin Zhang. 2020 · 2020
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Seqmix: Augmenting active sequence labeling via sequence mixup
Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020a · 2020
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AEDA: an easier data augmentation technique for text classification
Akbar Karimi, Leonardo Rossi, and Andrea Prati. 2021 · 2021
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Lexicon enhanced chinese sequence labeling using BERT adapter
Wei Liu, Xiyan Fu, Yue Zhang, and Wenming Xiao. 2021 · 2021
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Locate and label: A two-stage identifier for nested named entity recognition
Yongliang Shen, Xinyin Ma, Zeqi Tan, Shuai Zhang, Wen Wang, and Weiming Lu. 2021 · 2021
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Better feature integration for named entity recognition
Lu Xu, Zhanming Jie, Wei Lu, and Lidong Bing. 2021 · 2021
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