@inproceedings{xu-etal-2024-streamlined,
    title = "A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition",
    author = "Xu, Wenjie  and
      Chen, Yidan  and
      Ouyang, Jianquan",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.147",
    pages = "1673--1683",
    abstract = "Few-shot named entity recognition (NER) is a challenging task that aims to recognize new named entities with only a limited amount of labeled examples. In this paper, we introduce SSF, which is a streamlined span-based factorization method that addresses the problem of few-shot NER. Our approach formulates few-shot NER as a span-level alignment problem between query and support instances. To achieve this goal, SSF decomposes the span-level alignment problem into several refined span-level procedures. The proposed approach encompasses several key modules such as the Span Boosting Module, Span Prototypical Module, Span Alignment Module, and Span Optimization Module. Our experimental results demonstrate a significant improvement over the previous state-of-the-art performance. Specifically, compared to previous methods, our proposed approach achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on the SNIPS dataset. Moreover, our approach has surpassed the latest state-of-the-art performance on both datasets.",
}

Few-shot named entity recognition (NER) is a challenging task that aims to recognize new named entities with only a limited amount of labeled examples. In this paper, we introduce SSF, which is a streamlined span-based factorization method that addresses the problem of few-shot NER. Our approach formulates few-shot NER as a span-level alignment problem between query and support instances. To achieve this goal, SSF decomposes the span-level alignment problem into several refined span-level procedures. The proposed approach encompasses several key modules such as the Span Boosting Module, Span Prototypical Module, Span Alignment Module, and Span Optimization Module. Our experimental results demonstrate a significant improvement over the previous state-of-the-art performance. Specifically, compared to previous methods, our proposed approach achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on the SNIPS dataset. Moreover, our approach has surpassed the latest state-of-the-art performance on both datasets.
Xu, Wenjie Chen, Yidan Ouyang, Jianquan
A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition