---
title: "TPLinker — paper by Wang et al. (2020) — summary and ideas"
description: "The authors propose TPLinker, a single-stage model that treats extraction as linking pairs of tokens. 25 references, cited by 72 papers here."
authors: ["Yucheng Wang", "Bowen Yu", "Yueyang Zhang", "Tingwen Liu", "Hongsong Zhu", "Limin Sun"]
year: 2020
venue: "Proceedings of the 28th International Conference on Computational Linguistics"
source: "https://aclanthology.org/2020.coling-main.138/"
doi: "10.18653/v1/2020.coling-main.138"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2020-tplinker-single-stage-joint-extraction/"
---
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# TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking

**Authors:** Yucheng Wang, [Bowen Yu](https://md.research.elamur.ai/free/en/authors/bowen-yu/), Yueyang Zhang, [Tingwen Liu](https://md.research.elamur.ai/free/en/authors/tingwen-liu/), Hongsong Zhu and Limin Sun · **Year:** 2020 · **Venue:** [Proceedings of the 28th International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2020](https://md.research.elamur.ai/free/en/venues/coling/2020/)) · **Publisher:** [International Committee on Computational Linguistics (ICCL)](https://md.research.elamur.ai/free/en/publishers/international-committee-on-computational-linguistics-iccl/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.coling-main.138/) · [PDF](https://aclanthology.org/2020.coling-main.138.pdf) · [DOI 10.18653/v1/2020.coling-main.138](https://doi.org/10.18653/v1/2020.coling-main.138) · [OpenAlex](https://openalex.org/W3116427155)

**Citations:** 457 citations (OpenAlex, 2026-10-14) · [72](https://md.research.elamur.ai/free/en/papers/wang-2020-tplinker-single-stage-joint-extraction/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper addresses joint extraction of entities and relational triplets from text, focusing on overlapping cases where triplets share one entity (SEO) or both entities (EPO). Earlier joint models either generate triplets step by step with a decoder or first find subjects and then their objects. Both approaches train on gold context but must rely on their own predictions at test time, so errors accumulate (exposure bias). The authors propose TPLinker, a single-stage model that treats extraction as linking pairs of tokens. Its handshaking tagging scheme uses three kinds of links: entity head to entity tail, subject head to object head, and subject tail to object tail. The two subject–object link types are tagged separately for each relation. No step depends on an earlier prediction, so training and inference behave the same way. On the NYT and WebNLG benchmarks, TPLinker reports state-of-the-art results. Its largest gains are on sentences with overlapping relations or many triplets, and it also decodes faster than the CasRel baseline. This matters for building knowledge bases from realistic, densely related text.

## Key points
- The task is split into 2N+1 sequence-labeling subtasks, where N is the number of relation types. The entity-boundary matrix is shared across relations. Each tag sequence covers the upper triangle of the token-pair matrix, with length (n²+n)/2.
- The scheme handles nested entities and both SEO and EPO overlaps by design, for example "New York" and "New York City" sharing the object "De Blasio".
- TPLinker outperforms CasRel by 6.3 F1 points on sentences containing 5 or more triplets.
- TPLinker_BERT decodes about 3.6× faster than CasRel_BERT because it supports batched inference:
  - NYT: 15.2 ms vs 54.0 ms per sample.
  - WebNLG: 25.6 ms vs 76.8 ms per sample.
- On WebNLG (171 relations, 5,019 training sentences), TPLinker roughly matches CasRel. The authors attribute this to near-saturated scores and to near-synonymous relation labels in the annotation. Code is published on GitHub.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Baidu](https://md.research.elamur.ai/free/en/companies/baidu/) — Company, Beijing, China · 40 works · 14,627 citations
- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 68 works · 22,753 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 32 works · 7,615 citations
- [Institute of Information Engineering](https://md.research.elamur.ai/free/en/labs/institute-of-information-engineering/) — Research institute, Beijing, China · 5 works · 1,299 citations

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## Cite

Cite as: Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu and Limin Sun. 2020. TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking. Proceedings of the 28th International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2020tplinker,
  title = {{TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking}},
  author = {Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu and Limin Sun},
  booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.coling-main.138},
  url = {https://doi.org/10.18653/v1/2020.coling-main.138}
}
```

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## References

25 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*