Overview of autofeed: an unsupervised learning system for generating webfeeds
Bora Cenk Gazen, Steven Minton · 2006
The AutoFeed system automatically extracts data from semi-structured web sites. Previously, researchers have developed two types of supervised learning approaches for extracting web data: methods that create precise, site-specific extraction rules and methods that learn less-precise site-independent ex-traction rules. In either case, significant training is required. AutoFeed follows a third, more ambitious approach, in which unsupervised learning is used to analyze sites and discover their structure. Our method relies on a set of heterogeneous “experts”, each of which is capable of identifying certain types of generic structure. Each expert represents its discov-eries as “hints”. Based on these hints, our system clusters the pages and identifies semi-structured data that can be ex-tracted. To identify a good clustering, we use a probabilistic model of the hint-generation process. This paper summarizes our formulation of the fully-automatic web-extraction prob-lem, our clustering approach, and our results on a set of ex-periments.