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- W2080132606 abstract "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." @default.
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- W2080132606 date "2011-01-01" @default.
- W2080132606 modified "2023-10-18" @default.
- W2080132606 title "Automatic wrappers for large scale web extraction" @default.
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- W2080132606 doi "https://doi.org/10.14778/1938545.1938547" @default.
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