A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing approaches to few-shot named entity recognition require large amounts of labeled data. |
| Approach: | They propose a streamlined span-based factorization method that solves few-shot NER problem . they propose to decompose the span-level alignment problem into several refined procedures . |
| Outcome: | The proposed method achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on SNIPS dataset. |
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| Challenge: | Existing methods for few-shot nested named entity recognition (NER) ignore relationship between inner and outer entities, which is crucial for fewshot ner. |
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Ruotian Ma, Zhang Lin, Xuanting Chen, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen
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| Challenge: | Named entity recognition (NER) is widely adopted in several domains, such as news, medical, and social media. |
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| Challenge: | Named entity recognition systems aim at recognizing unseen entity types based on a few labeled examples. |
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Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)
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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
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