Automatic Wrappers for Large Scale Web Extraction
  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. 
Nilesh Dalvi (Yahoo! Research), Ravi Kumar (Yahoo! Research), Mohamed   Soliman (U. of Waterloo)
cs.DB