  We present a generic framework to make wrapper induction algorithms tolerant
to noise in the training data. This enables us to learn wrappers in a
completely unsupervised manner from automatically and cheaply obtained noisy
training data, e.g., using dictionaries and regular expressions. By removing
the site-level supervision that wrapper-based techniques require, we are able
to perform information extraction at web-scale, with accuracy unattained with
existing unsupervised extraction techniques. Our system is used in production
at Yahoo! and powers live applications.
