Automatic wrappers for large scale web extraction

Automatic wrappers for large scale web extraction (2011)

by Nilesh Dalvi , Ravi Kumar , Mohamed Soliman

Venue:	VLDB Endowment
Citations:	4 - 1 self

Datum	Value	Source
TITLE	Automatic wrappers for large scale web extraction	INFERENCE
AUTHOR NAME	Nilesh Dalvi	SVM HeaderParse 0.2
AUTHOR AFFIL	Yahoo! Research	SVM HeaderParse 0.2
AUTHOR ADDR	Santa Clara, CA, USA	SVM HeaderParse 0.2
AUTHOR NAME	Ravi Kumar	SVM HeaderParse 0.2
AUTHOR AFFIL	Yahoo! Research	SVM HeaderParse 0.2
AUTHOR ADDR	Santa Clara, CA, USA	SVM HeaderParse 0.2
AUTHOR NAME	Mohamed Soliman	SVM HeaderParse 0.2
AUTHOR AFFIL	U. of Waterloo	SVM HeaderParse 0.2
AUTHOR ADDR	Ontario, Canada	SVM HeaderParse 0.2
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. 1.	SVM HeaderParse 0.2
YEAR	2011	INFERENCE
VENUE	VLDB Endowment	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	230	INFERENCE
CITATIONS	21 found	ParsCit 1.0