Papers › Extracting Entities and Relations with Joint Minimum Risk Training
Extracting Entities and Relations with Joint Minimum Risk Training
Changzhi Sun, Yuanbin Wu, Man Lan, Shiliang Sun, Wenting Wang, Kuang-Chih Lee, Kewen Wu
We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | ACE 2005 | MRT | Cross Sentence | No | #18 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | MRT | NER Micro F1 | 83.6 | #18 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | MRT | RE+ Micro F1 | 59.6 | #18 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | MRT | Sentence Encoder | biLSTM | #18 of 30 | 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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