Research map / Label-efficient and robust learning / Noisy labels
Data Programming: Creating Large Training Sets, Quickly
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace first-match if-then-return labels with data programming | Hand-tuned features · KBP (News) | F1: 1.92 | Helped |
| Replace first-match if-then-return labels with data programming | LSTM features · KBP (News) | F1: 3.12 | Helped |
| Replace first-match if-then-return labels with data programming | Hand-tuned features · Genomics | F1: 1.59 | Helped |
| Replace first-match if-then-return labels with data programming | LSTM features · Genomics | F1: 0.47 | Helped |
| Replace first-match if-then-return labels with data programming | Hand-tuned features · Pharmacogenomics | F1: 3.60 | Helped |
| Replace first-match if-then-return labels with data programming | LSTM features · Pharmacogenomics | F1: 4.94 | Helped |
| Use data programming rather than the state-of-the-art LSTM approach | LSTM relation extraction · KBP (News), 2014 TAC-KBP | F1: 5.98 point F1 score gain | Helped |
| Add dependencies among labeling functions | Data programming disease mention extraction · Diseases | F1: 2.3 point F1 score boost | Helped |
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