# AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction

arXiv: https://arxiv.org/abs/2406.10432 · HTML: https://mlsanity.com/paper/2406.10432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again](https://mlsanity.com/paper/2203.08410/) (2022)
- [UDapter: Language Adaptation for Truly Universal Dependency Parsing](https://mlsanity.com/paper/2004.14327/) (2020)
- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [Pushing the Limits of ChatGPT on NLP Tasks](https://mlsanity.com/paper/2306.09719/) (2023)
- [Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction](https://mlsanity.com/paper/2311.05922/) (2023)
- [Semantic Structure Enhanced Event Causality Identification](https://mlsanity.com/paper/2305.12792/) (2023)
- [Language Models can Exploit Cross-Task In-context Learning for Data-Scarce Novel Tasks](https://mlsanity.com/paper/2405.10548/) (2024)
- [Multilingual AMR Parsing with Noisy Knowledge Distillation](https://mlsanity.com/paper/2109.15196/) (2021)
- [Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction](https://mlsanity.com/paper/2210.11800/) (2022)
- [Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction](https://mlsanity.com/paper/2404.17809/) (2024)
- [Linguistic representations for fewer-shot relation extraction across domains](https://mlsanity.com/paper/2307.03823/) (2023)

## Built on by (most cited first, 1 of 1)

- [How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing](https://mlsanity.com/paper/2610.13236/) (2026)

## Abstract (arXiv)

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.