Existing in-context learning (ICL) methods for relation extraction (RE) often prioritize language similarity over structural similarity, which can lead to overlooking entity relationships. To address this, we propose an AMR-enhanced retrieval-based ICL method for RE. Our model retrieves in-context examples based on semantic structure similarity between task inputs and training samples. Evaluations on four standard English RE datasets show that our model outperforms baselines in the unsupervised setting across all datasets. In the supervised setting, it achieves state-of-the-art results on three datasets and competitive results on the fourth.
@article{arxiv.2406.10432,
title = {AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction},
author = {Peitao Han and Lis Kanashiro Pereira and Fei Cheng and Wan Jou She and Eiji Aramaki},
journal= {arXiv preprint arXiv:2406.10432},
year = {2025}
}