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Are Noisy Sentences Useless for Distant Supervised Relation Extraction?
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DCRE to relabel high-confidence noisy sentences through deep clustering instead of only down-weighting or removing them | Distant supervision relation extraction · NYT-10 | precision-recall performance | Helped |
| Set the noise-detection threshold to φ = 0.1 (ablation) | DCRE · NYT-10 | precision-recall performance | Helped |
| Set the noise-detection threshold to φ = 0 (ablation) | DCRE · NYT-10 | precision-recall performance | Hurt |
| Vary the noise-detection threshold among φ = 0.15, 0.1, 0.05, and 0 (ablation) | DCRE · NYT-10 | precision-recall performance | Mixed |
Closest papers by meaning
- Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector (AAAI 2019 · similarity 0.91)
- Bootstrapping Distantly Supervised IE Using Joint Learning and Small Well-Structured Corpora (AAAI 2017 · similarity 0.90)
- Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding (AAAI 2019 · similarity 0.89)
- Aggregating Inter-Sentence Information to Enhance Relation Extraction (AAAI 2016 · similarity 0.89)
- Large Scaled Relation Extraction With Reinforcement Learning (AAAI 2018 · similarity 0.89)
- A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling (AAAI 2024 · similarity 0.87)
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