Wrapper Generation Supervised by a Noisy Crowd
Valter Crescenzi, Paolo Merialdo, Disheng Qiu · 2013
We present solutions based on crowdsourcing platforms to support large-scale production of accurate wrappers around data-intensive websites. Our approach is based on super-vised wrapper induction algorithms which demand the bur-den of generating the training data to the workers of a crowdsourcing platform. Workers are paid for answering simple membership queries chosen by the system. We present two algorithms: a single worker algorithm (alfη) and a mul-tiple workers algorithm (alfred). Both the algorithms deal with the inherent uncertainty of the responses and use an ac-tive learning approach to select the most informative queries. alfred estimates the workers ’ error rate to decide at run-time how many workers are needed. The experiments that we conducted on real and synthetic data are encouraging: our approach is able to produce accurate wrappers at a low cost, even in presence of workers with a significant error rate. 1.