Papers with distant supervision relation extraction
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction (P18-1)
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| Challenge: | Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. |
| Approach: | They propose a sentence-level true-positive generator to learn a true-negative generator from a fuzzy sentence bag. |
| Outcome: | The proposed method significantly improves the performance of distant supervision relation extraction compared to state-of-the-art systems. |
H-FND: Hierarchical False-Negative Denoising for Distant Supervision Relation Extraction (2021.findings-acl)
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| Challenge: | Existing work on distant supervision denoising introduces false-positive (FP) and falsenegative (FN) training instances to the generated datasets. |
| Approach: | They propose a hierarchical false-negative denoising framework for distant supervision relation extraction that denoises false-positive and false- negative training instances. |
| Outcome: | The proposed framework can revise FN instances correctly and maintain high F1 scores even when 50% of instances have been turned into negatives. |