# TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking

arXiv: https://arxiv.org/abs/2010.13415 · HTML: https://mlsanity.com/paper/2010.13415/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 30 of 72)

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- [A Partition Filter Network for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2108.12202/) (2021)
- [PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction](https://mlsanity.com/paper/2106.09895/) (2021)
- [OneRel:Joint Entity and Relation Extraction with One Module in One Step](https://mlsanity.com/paper/2203.05412/) (2022)
- [Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis](https://mlsanity.com/paper/2203.10796/) (2022)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction](https://mlsanity.com/paper/2209.02693/) (2022)
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- [A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling](https://mlsanity.com/paper/2109.06705/) (2021)
- [Revisiting the Negative Data of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.10158/) (2021)
- [CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction](https://mlsanity.com/paper/2107.01583/) (2021)
- [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)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [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)
- [Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models](https://mlsanity.com/paper/2310.05628/) (2023)
- [Text2MDT: Extracting Medical Decision Trees from Medical Texts](https://mlsanity.com/paper/2401.02034/) (2024)
- [AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction](https://mlsanity.com/paper/2409.01854/) (2024)
- [Query-based Instance Discrimination Network for Relational Triple Extraction](https://mlsanity.com/paper/2211.01797/) (2022)
- [A Unified One-Step Solution for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2306.04152/) (2023)
- [Event Causality Extraction with Event Argument Correlations](https://mlsanity.com/paper/2301.11621/) (2023)
- [Sequence-Based Extractive Summarisation for Scientific Articles](https://mlsanity.com/paper/2204.03301/) (2022)

## Abstract (arXiv)

Extracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying overlapping relations with shared entities. Prior works show that joint learning can result in a noticeable performance gain. However, they usually involve sequential interrelated steps and suffer from the problem of exposure bias. At training time, they predict with the ground truth conditions while at inference it has to make extraction from scratch. This discrepancy leads to error accumulation. To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias. TPLinker formulates joint extraction as a token pair linking problem and introduces a novel handshaking tagging scheme that aligns the boundary tokens of entity pairs under each relation type. Experiment results show that TPLinker performs significantly better on overlapping and multiple relation extraction, and achieves state-of-the-art performance on two public datasets.