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We present a bi-encoder framework for named entity recognition (NER), which applies contrastive learning to map candidate text spans and entity types into the same vector representation space.
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Wenkai Zhang, Hongyu Lin, Xianpei Han, and Le Sun · 2021
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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
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Juncheng Wan, Dongyu Ru, Weinan Zhang, and Yong Yu · 2022
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Shuai Zhang, Yongliang Shen, Zeqi Tan, Yiquan Wu, and Weiming Lu · 2022
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Distantly supervised named entity recognition via confidence-based multi-class positive and unlabeled learning
Kang Zhou, Yuepei Li, and Qi Li · 2022
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