Papers › TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu, Limin Sun
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | NYT10-HRL | TPLinker | F1 | 72.45 | #3 of 10 | Archive leaderboard | report |
| Relation Extraction | NYT10-HRL | TPLinker | F1 | 71.93 | #5 of 10 | Archive leaderboard | report |
| Relation Extraction | NYT11-HRL | TPLinker | F1 | 55.67 | #2 of 12 | Archive leaderboard | report |
| Relation Extraction | NYT11-HRL | TPLinker | F1 | 55.28 | #4 of 12 | Archive leaderboard | report |
| Relation Extraction | WebNLG | TPLinker | F1 | 91.9 | #6 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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