ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision. (arXiv:2204.06863v1 [cs.LG])

A way to overcome expensive and time-consuming manual data labeling is weak
supervision - automatic annotation of data samples via a predefined set of
labeling functions (LFs), rule-based mechanisms that generate potentially
erroneous labels. In this work, we investigate noise reduction techniques for
weak supervision based on the principle of k-fold cross-validation. In
particular, we extend two frameworks for detecting the erroneous samples in
manually annotated data to the weakly supervised setting. Our methods profit
from leveraging the information about …

arxiv function labeling unsupervised validation