Active Learning for Hierarchical Wrapper Induction

As an alternative to manually writing extraction rules, we created STALKER, which is a wrapper induction algorithm that learns high-accuracy extraction rules. The major novelty introduced by STALKER is the concept of hierarchical wrapper induction: the extraction of the relevant data is performed in a hierarchical manner based on the embedded catalog tree (ECT), which is a user-provided description of the information to be extracted.

Authors

• Ion Muslea Thomas J. Lee
• Steve Minton
• Craig Knoblock
• University of Southern California

Keywords

No keywords are indexed for this paper.