Using latent-structure to detect objects on the web

An important requirement for emerging applications which aim to locate and integrate content distributed over the Web is to identify pages that are relevant for a given domain or task. In this paper, we address the problem of identifying pages that contain objects with a latent structure, i.e., the structure is implicitly represented in the page. We propose an algorithm which, given a set of instances of an object type, derives rules by automatically extracting statistically significant patterns present inside the objects. These rules can then be used to detect the presence of these objects in new, unseen pages. Our approach has several advantages when compared against learning-based text classifiers. Because it relies only on positive examples, constructing accurate object detectors is simpler than constructing learning classifiers, which require both positive and negative examples. Also, besides providing a classification decision for the presence of an object, the derived detectors are able to pinpoint the location of the object inside a Web page. This enables our algorithm to extract additional object fragments and apply online learning to automatically update the rules as new documents become available. An experimental evaluation, using a representative set of domains, indicates that our approach is effective. It is able to learn structural patterns and derive detectors that outperform state-of-art text classifiers and the online learning component leads to substantial improvements over the initial detectors.