Learning to remove Internet advertisements
Proceedings of the third annual conference on Autonomous Agents
Template detection via data mining and its applications
Proceedings of the 11th international conference on World Wide Web
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Eliminating noisy information in Web pages for data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning block importance models for web pages
Proceedings of the 13th international conference on World Wide Web
The volume and evolution of web page templates
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Automatic Identification of Informative Sections of Web Pages
IEEE Transactions on Knowledge and Data Engineering
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
Template detection for large scale search engines
Proceedings of the 2006 ACM symposium on Applied computing
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Web page cleaning for web mining through feature weighting
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Coherent keyphrase extraction via web mining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Detection of template and noise blocks in web pages is an important step in improving the performance of information retrieval and content extraction. Of the many approaches proposed, most rely on the assumption of operating within the confines of a single website or require expensive hand-labeling of relevant and non-relevant blocks for model induction. This reduces their applicability, since in many practical scenarios template blocks need to be detected in arbitrary web pages, with no prior knowledge of the site structure. In this work we propose to bridge these two approaches by using within-site template discovery techniques to drive the induction of a site-independent template detector. Our approach eliminates the need for human annotation and produces highly effective models. Experimental results demonstrate the usefulness of the proposed methodology for the important applications of keyword extraction, with relative performance gain as high as 20%.