In this paper, we describe a system that can extract recordstructures from web pages with no direct human supervision.Records are commonly occurring HTML-embedded data tuples that describe people, offered courses, products,company profiles, etc. We present a simplified frameworkfor studying the problem of unsupervised record extraction. one which separates the algorithms from the feature engineering.Our system, U-REST formalizes an approach tothe problem of unsupervised record extraction using a simple two-stage machine learning framework. The first stage involves clustering, where structurally similar regions are discovered, and the second stage involves classification, where discovered groupings (clusters of regions) are ranked by their likelihood of being records. In our work, we describe, and summarize the results of an extensive survey of features for both stages. We conclude by comparing U-REST to related systems. The results of our empirical evaluation show encouraging improvements in extraction accuracy.
['Yuan Kui Shen', 'David R. Karger']
U-REST: an unsupervised record extraction system