Page-level template detection via isotonic smoothing

We develop a novel framework for the page-level template detection problem. Our framework is built on two main ideas. The first is the automatic generation of training data for a classifier that, given a page, assigns a templateness score to every DOM node of the page. The second is the global smoothing of these per-node classifier scores by solving a regularized isotonic regression problem; the latter folm a simple yet powerful abstraction of templateness on a page. Our extensive experiments on human-labeled test data show that our approach detects templates effectively. Categories and Subject Descriptors H.4.m [Information Systems]: Miscellaneous General Terms Algorithms, Experimentation, Measurements Keywords Webpage sectioning, webpage segmentation, template detection, isotonic regression

• Type: Conference
• Year: 2007
• Where: WWW
• Authors: Deepayan Chakrabarti, Ravi Kumar, Kunal Punera
• Keywords: Internet Technology, Page-level Template Detection, Per-node Classifier Scores, Regularized Isotonic Regression, WWW 2007