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2007
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U-REST: an unsupervised record extraction system

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U-REST: an unsupervised record extraction system
In this paper, we describe a system that can extract record structures 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 framework for studying the problem of unsupervised record extraction ? one which separates the algorithms from the feature engineering. Our system, U-REST formalizes an approach to the problem of unsupervised record extraction using a simple a 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 ...
Yuan Kui Shen, David R. Karger
Added 22 Nov 2009
Updated 22 Nov 2009
Type Conference
Year 2007
Where WWW
Authors Yuan Kui Shen, David R. Karger
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