@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://aclanthology.org/2025.naacl-srw.33/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction
%A Han, Peitao
%A Pereira, Lis
%A Cheng, Fei
%A She, Wan Jou
%A Aramaki, Eiji
%Y Ebrahimi, Abteen
%Y Haider, Samar
%Y Liu, Emmy
%Y Haider, Sammar
%Y Leonor Pacheco, Maria
%Y Wein, Shira
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-192-6
%F han-etal-2025-amr
%X 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.
%R 10.18653/v1/2025.naacl-srw.33
%U https://aclanthology.org/2025.naacl-srw.33/
%U https://doi.org/10.18653/v1/2025.naacl-srw.33
%P 333-342
Markdown (Informal)
[AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction](https://aclanthology.org/2025.naacl-srw.33/) (Han et al., NAACL 2025)
ACL