Few-shot Relation Extraction

Few-Shot Relation Extraction is a relation extraction paradigm that addresses the identification of semantic relations between entities in text using a minimal number of labeled instances per relation, typically leveraging meta-learning or transfer learning to generalize from limited supervision.

62

Publications

2.9K

Citations

226

Authors

88

Institutions

Publications per year

2017–2026

62

Authors

226

  1. HC
    Huajun Chen

    Zhejiang University, China

    3 publications · 454 citations

    3

    h-index

  2. FH
    Fei Huang

    Alibaba Group (United States), United States

    3 publications · 419 citations

    3

    h-index

  3. NZ
    Ningyu Zhang

    Zhejiang University, China

    3 publications · 447 citations

    3

    h-index

  4. ZL
    Zhiyuan Liu

    Tsinghua University, China

    3 publications · 358 citations

    3

    h-index

  5. LS
    Luo Si

    Purdue University West Lafayette, United States

    3 publications · 419 citations

    3

    h-index

1–5 of 226

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 239 citations

    7

    h-index

  2. 4

    h-index

  3. Artificial Intelligence Review

    2 publications · 51 citations

    3

    h-index

  4. 2

    h-index

  5. 2

    h-index

1–5