document: U-REST : an unsupervised record extraction system 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 .
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