@inproceedings{han-etal-2025-amr,
    title = "{AMR}-{RE}: {A}bstract {M}eaning {R}epresentations for Retrieval-Based In-Context Learning in Relation Extraction",
    author = "Han, Peitao  and
      Pereira, Lis  and
      Cheng, Fei  and
      She, Wan Jou  and
      Aramaki, Eiji",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-srw.33/",
    pages = "333--342",
    ISBN = "979-8-89176-192-6",
    abstract = "Existing in-context learning (ICL) methods for relation extraction (RE) often prioritize language similarity over structural similarity, which may result in overlooking entity relationships. We propose an AMR-enhanced retrieval-based ICL method for RE to address this issue. Our model retrieves in-context examples based on semantic structure similarity between task inputs and training samples. We conducted experiments in the supervised setting on four standard English RE datasets. The results show that our method achieves state-of-the-art performance on three datasets and competitive results on the fourth. Furthermore, our method outperforms baselines by a large margin across all datasets in the more demanding unsupervised setting."
}