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. |
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