Research map / Graph learning / Knowledge and temporal graphs
Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add sentence-level attention over bag instances (ablation) | PCNNs+MIL · NYT-Freebase manual eval average | precision: 0.783 -> 0.803 | Helped |
| add entity descriptions (ablation) | PCNNs+MIL · NYT-Freebase manual eval average | precision: 0.783 -> 0.797 | Helped |
| add entity descriptions (ablation) | APCNNs · NYT-Freebase manual eval average | precision: 0.803 -> 0.813 | Helped |
| add attention and entity descriptions | PCNNs+MIL · NYT-Freebase held-out | recall: 0.34 -> 0.37 | Helped |
Closest papers by meaning
- Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding (AAAI 2019 · similarity 0.92)
- Global Distant Supervision for Relation Extraction (AAAI 2016 · similarity 0.92)
- SEE: Syntax-Aware Entity Embedding for Neural Relation Extraction (AAAI 2018 · similarity 0.92)
- DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning (ICML 2025 · similarity 0.88)
- Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction (AAAI 2024 · similarity 0.87)
- A Hierarchical Network for Multimodal Document-Level Relation Extraction (AAAI 2024 · similarity 0.87)
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