Few Shot NER Annotated Paper

Few-Shot Named Entity Recognition: A Comprehensive Study

A lesser-known albeit important paper in my opinion. This paper highlights a key problem in the industry that does not always appear in research making it all the more impressive. In this paper, the authors talk about the problem of less data for NER in industry and experimentally try the effects of three key approaches on few-shot NER:

• Meta-Learning: Construct prototypes for different entities
• Supervised pre-training on huge noisy data
• Self Training

Please feel free to read along with the paper with my notes and highlights.

Color	Meaning
Green	Topics about the current paper
Yellow	Topics about other relevant references
Blue	Implementation details/ maths/experiments
Red	Text including my thoughts, questions, and understandings

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CITATION

@misc{huang2020fewshot,
      title={Few-Shot Named Entity Recognition: A Comprehensive Study}, 
      author={Jiaxin Huang and Chunyuan Li and Krishan Subudhi and Damien Jose and Shobana Balakrishnan and Weizhu Chen and Baolin Peng and Jianfeng Gao and Jiawei Han},
      year={2020},
      eprint={2012.14978},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}