Papers › Span-Level Model for Relation Extraction
Span-Level Model for Relation Extraction
Kalpit Dixit, Yaser Al-Onaizan
Relation Extraction is the task of identifying entity mention spans in raw text and then identifying relations between pairs of the entity mentions. Recent approaches for this span-level task have been token-level models which have inherent limitations. They cannot easily define and implement span-level features, cannot model overlapping entity mentions and have cascading errors due to the use of sequential decoding. To address these concerns, we present a model which directly models all possible spans and performs joint entity mention detection and relation extraction. We report a new state-of-the-art performance of 62.83 F1 (prev best was 60.49) on the ACE2005 dataset.
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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 | Span-level | Cross Sentence | No | #22 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | Span-level | NER Micro F1 | 85.98 | #22 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | Span-level | Sentence Encoder | ELMo | #22 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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