# Relation of the Relations: A New Paradigm of the Relation Extraction Problem

arXiv: https://arxiv.org/abs/2006.03719 · HTML: https://mlsanity.com/paper/2006.03719/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [Entity-centered Cross-document Relation Extraction](https://mlsanity.com/paper/2210.16541/) (2022)
- [SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues](https://mlsanity.com/paper/2106.01006/) (2021)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Document-level Relation Extraction with Relation Correlations](https://mlsanity.com/paper/2212.10171/) (2022)
- [UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering](https://mlsanity.com/paper/2104.04549/) (2021)

## Abstract (arXiv)

In natural language, often multiple entities appear in the same text. However, most previous works in Relation Extraction (RE) limit the scope to identifying the relation between two entities at a time. Such an approach induces a quadratic computation time, and also overlooks the interdependency between multiple relations, namely the relation of relations (RoR). Due to the significance of RoR in existing datasets, we propose a new paradigm of RE that considers as a whole the predictions of all relations in the same context. Accordingly, we develop a data-driven approach that does not require hand-crafted rules but learns by itself the RoR, using Graph Neural Networks and a relation matrix transformer. Experiments show that our model outperforms the state-of-the-art approaches by +1.12% on the ACE05 dataset and +2.55% on SemEval 2018 Task 7.2, which is a substantial improvement on the two competitive benchmarks.