A Fully Automated Object Extraction System for the World Wide Web
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Web data extraction based on partial tree alignment
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thresher: automating the unwrapping of semantic content from the World Wide Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Information extraction for search engines using fast heuristic techniques
Data & Knowledge Engineering
TEX: An efficient and effective unsupervised Web information extractor
Knowledge-Based Systems
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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.