Research map / Label-efficient and robust learning / Recognition and long-tail
Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add linear attenuation simulation and non-IID relevance embedding | PCNNs · NYT test (2007) | Average P@N (%): 72.2 -> 81.8 | Helped |
| Add linear attenuation simulation without non-IID relevance embedding (ablation) | PCNNs · NYT test (2007) | Average P@N (%): 72.2 -> 77.0 | Helped |
| Add non-IID relevance embedding without linear attenuation simulation (ablation) | PCNNs · NYT test (2007) | Average P@N (%): 72.2 -> 79.1 | Helped |
| Add non-IID relevance embedding to linear attenuation simulation (ablation) | PCNNs+W · NYT test (2007) | Average P@N (%): 77.0 -> 81.8 | Helped |
| Add linear attenuation simulation to non-IID relevance embedding (ablation) | PCNNs+N · NYT test (2007) | Average P@N (%): 79.1 -> 81.8 | Helped |
| Set the attenuation threshold D to 60 (ablation) | PCNNs+WN · NYT validation | precision | Helped |
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