  Motivated by the desire to generate labels for real-time data we develop a
method to estimate the dependency structure and accuracy of weak supervision
sources incrementally. Our method first estimates the dependency structure
associated with the supervision sources and then uses this to iteratively
update the estimated source accuracies as new data is received. Using both
off-the-shelf classification models trained using publicly-available datasets
and heuristic functions as supervision sources we show that our method
generates probabilistic labels with an accuracy matching that of existing
off-line methods.
