Research map / Label-efficient and robust learning / Recognition and long-tail
SEE: Syntax-Aware Entity Embedding for Neural Relation Extraction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add syntax-aware entity embeddings and TRANS relation scoring | Baseline neural relation extraction · Manual evaluation Top 100 | precision: 0.86 -> 0.91 | Helped |
| Add syntax-aware entity embeddings and TRANS relation scoring | Baseline neural relation extraction · Manual evaluation Top 200 | precision: 0.84 -> 0.87 | Helped |
| Add syntax-aware entity embeddings and TRANS relation scoring | Baseline neural relation extraction · Manual evaluation Top 500 | precision: 0.73 -> 0.77 | Helped |
| Add syntax-aware entity embeddings and TRANS relation scoring | Baseline neural relation extraction · Manual evaluation Average | precision: 0.810 -> 0.850 | Helped |
| Use TRANS instead of CAT to combine entity context representations (ablation) | SEE · NYT held-out evaluation | precision-recall performance | Helped |
| Replace attention over child embeddings with averaging (ablation) | SEE-TRANS · NYT held-out evaluation | precision-recall performance | Hurt |
| Replace attention over sentence-set entity embeddings with averaging (ablation) | SEE-TRANS · NYT held-out evaluation | precision-recall performance | Hurt |
Closest papers by meaning
- Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction (AAAI 2020 · similarity 0.93)
- Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions (AAAI 2017 · similarity 0.92)
- Large Scaled Relation Extraction With Reinforcement Learning (AAAI 2018 · similarity 0.90)
- Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction (AAAI 2021 · similarity 0.89)
- Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding (AAAI 2019 · similarity 0.89)
- Enhancing Low-Resource Relation Representations through Multi-View Decoupling (AAAI 2024 · similarity 0.87)
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