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.
@article{arxiv.1103.2406,
title = {Automatic Wrappers for Large Scale Web Extraction},
author = {Nilesh Dalvi and Ravi Kumar and Mohamed Soliman},
journal= {arXiv preprint arXiv:1103.2406},
year = {2011}
}