Weak Supervision

Weak supervision provides an alternative or complementary strategy to manual annotation in projects where labeling cost is high. Approximate labels can be generated through rule sets, distant supervision sources, dictionaries, or weak signals. These labels are not perfect, but they are highly valuable for bootstrapping large-scale training data. This approach is especially powerful in NLP, log analysis, and document processing. The quality of weak labels should be monitored carefully and ideally calibrated against gold-standard data.