Automatic Wrappers for Large Scale Web Extraction

@article{Dalvi2011AutomaticWF,
  title={Automatic Wrappers for Large Scale Web Extraction},
  author={Nilesh N. Dalvi and Ravi Kumar and Mohamed A. Soliman},
  journal={ArXiv},
  year={2011},
  volume={abs/1103.2406}
}
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… 

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