Adapting Information Extraction Knowledge For Unseen Web Sites Tak-Lam Wong and Wai Lam   View Paper (PDF)   Return to Main-Track Regular Papers Abstract We propose a wrapper adaptation framework which aims at adapting a learned wrapper to an unseen Web site. It significantly reduces human effort in constructing wrappers. Our framework makes use of extraction rules previously discovered from a particular site to seek potential training ex-ample candidates for an unseen site. Rule generalization and text categorization are employed for finding suitable example candidates. Another feature of our approach is that it makes use of the previously discovered lexicon to classify good training examples automatically for the new site. We conducted extensive experiments to evaluate the quality of the extraction performance and the adaptability of our approach. DiSC'03 © 2003 Association for Computing Machinery