Research map / Reinforcement learning / Visual and representation RL
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use hierarchical relation detection followed by entity extraction | Relation extraction · NYT10 | micro-F1: .522 -> .644 | Helped |
| Use hierarchical relation detection followed by entity extraction | Relation extraction · NYT11 | micro-F1: .531 -> .538 | Helped |
| Use hierarchical relation detection followed by entity extraction | Overlapping relation extraction · NYT10-sub | micro-F1: .315 -> .600 | Helped |
| Use hierarchical relation detection followed by entity extraction | Overlapping relation extraction · NYT11-plus | micro-F1: .307 -> .372 | Helped |
| Add the low-level entity extraction policy (ablation) | HRL relation detection · NYT11 | relation detection F1: .676 -> .654 | No clear effect |
| Add the low-level entity extraction policy (ablation) | HRL relation detection · NYT11-plus | relation detection F1: .413 -> .527 | Helped |
Closest papers by meaning
- Large Scaled Relation Extraction With Reinforcement Learning (AAAI 2018 · similarity 0.91)
- Enhancing Relation Extraction via Supervised Rationale Verification and Feedback (AAAI 2025 · similarity 0.87)
- Relation-R1: Progressively Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relation Comprehension (AAAI 2026 · similarity 0.86)
- A Hierarchical Network for Multimodal Document-Level Relation Extraction (AAAI 2024 · similarity 0.85)
- Continual Relation Extraction via Sequential Multi-Task Learning (AAAI 2024 · similarity 0.84)
- Reliable Data Generation and Selection for Low-Resource Relation Extraction (AAAI 2024 · similarity 0.84)
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