Term-weighting approaches in automatic text retrieval
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Improved algorithms for topic distillation in a hyperlinked environment
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Automatic discovery of language models for text databases
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Finding related pages in the World Wide Web
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Trawling the Web for emerging cyber-communities
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Recognizing structure in Web pages using similarity queries
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Agglomerative clustering of a search engine query log
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A vector space model for automatic indexing
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QProber: A system for automatic classification of hidden-Web databases
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AutoFeed: an unsupervised learning system for generating webfeeds
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A web content manipulation technique based on page Fragmentation
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Routing Queries through a Peer-to-Peer InfoBeacons Network Using Information Retrieval Techniques
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In this paper, we introduce the concept of a QA-Pageletto refer to the content region in a dynamic page that containsquery matches. We present THOR, a scalable andefficient mining system for discovering and extracting QA-Pageletsfrom the Deep Web. A unique feature of THOR isits two-phase extraction framework. In the first phase, pagesfrom a deep web site are grouped into distinct clusters ofstructurally-similar pages. In the second phase, pages fromeach page cluster are examined through a subtree filteringalgorithm that exploits the structural and content similarityat subtree level to identify the QA-Pagelets.