# PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction

arXiv: https://arxiv.org/abs/2106.09895 · HTML: https://mlsanity.com/paper/2106.09895/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction](https://mlsanity.com/paper/1911.09886/) (2019)
- [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://mlsanity.com/paper/2010.13415/) (2020)
- [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018)
- [Relation Extraction : A Survey](https://mlsanity.com/paper/1712.05191/) (2017)
- [Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy](https://mlsanity.com/paper/1909.04273/) (2019)
- [CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases](https://mlsanity.com/paper/1610.08763/) (2016)
- [Finding Influential Instances for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2009.09841/) (2020)

## Built on by (most cited first, 30 of 40)

- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [OntoProtein: Protein Pretraining With Gene Ontology Embedding](https://mlsanity.com/paper/2201.11147/) (2022)
- [Universal Information Extraction as Unified Semantic Matching](https://mlsanity.com/paper/2301.03282/) (2023)
- [LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model](https://mlsanity.com/paper/2304.06248/) (2023)
- [OneRel:Joint Entity and Relation Extraction with One Module in One Step](https://mlsanity.com/paper/2203.05412/) (2022)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Uncovering Main Causalities for Long-tailed Information Extraction](https://mlsanity.com/paper/2109.05213/) (2021)
- [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://mlsanity.com/paper/2201.03335/) (2022)
- [ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget](https://mlsanity.com/paper/2408.00103/) (2024)
- [UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction](https://mlsanity.com/paper/2402.06861/) (2024)
- [A Simple but Effective Bidirectional Framework for Relational Triple Extraction](https://mlsanity.com/paper/2112.04940/) (2021)
- [UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction](https://mlsanity.com/paper/2211.09039/) (2022)
- [Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning](https://mlsanity.com/paper/2205.02355/) (2022)
- [Towards Relation Extraction From Speech](https://mlsanity.com/paper/2210.08759/) (2022)
- [FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework](https://mlsanity.com/paper/2205.02490/) (2022)
- [RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction](https://mlsanity.com/paper/2304.14770/) (2023)
- [Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction](https://mlsanity.com/paper/2210.10709/) (2022)
- [Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study](https://mlsanity.com/paper/2210.10678/) (2022)
- [Query-based Instance Discrimination Network for Relational Triple Extraction](https://mlsanity.com/paper/2211.01797/) (2022)
- [Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning](https://mlsanity.com/paper/2201.04826/) (2022)
- [Schema-adaptable Knowledge Graph Construction](https://mlsanity.com/paper/2305.08703/) (2023)
- [Untargeted Adversarial Attack on Knowledge Graph Embeddings](https://mlsanity.com/paper/2405.10970/) (2024)
- [Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion](https://mlsanity.com/paper/2408.03079/) (2024)
- [Relational Triple Extraction: One Step is Enough](https://mlsanity.com/paper/2205.05270/) (2022)
- [PCRED: Zero-shot Relation Triplet Extraction with Potential Candidate Relation Selection and Entity Boundary Detection](https://mlsanity.com/paper/2211.14477/) (2022)
- [Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2109.05182/) (2021)
- [A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction](https://mlsanity.com/paper/2203.13953/) (2022)
- [NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction](https://mlsanity.com/paper/2204.00255/) (2022)
- [90% F1 Score in Relational Triple Extraction: Is it Real ?](https://mlsanity.com/paper/2302.09887/) (2023)

## Abstract (arXiv)

Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples.