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Information retrieval / Control theory / Library science / Statistical natural language processing / Relevance / Tf*idf / Ontology / Feedback / Library of Congress Subject Headings / Science / Information science / Information


Mining Specific and General Features in Both Positive and Negative Relevance Feedback QUT E-Discovery Lab at the TREC’09 Relevance Feedback Track Yuefeng Li, Xiaohui Tao, Abdulmohsen Algarni† , Sheng-Tang Wu∗ Schoo
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Document Date: 2010-02-03 08:27:29

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Company

Google / Phase 1 / /

Country

Germany / /

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Event

FDA Phase / /

Facility

Asia University / Queensland University of Technology / Library of Congress Subject Headings1 / QUT library / /

IndustryTerm

Web information gathering / Web documents / pattern mining technique / Web users / text pre-processing / title search / possible solution / relevance feedback algorithms / pattern taxonomy mining / Web / personalized Web information gathering / Web user profile acquisition / relevance feedback algorithm / Web systems / pattern mining techniques / /

MarketIndex

SET 100 / bel / /

Organization

Congress / Sheng-Tang Wu∗ School of Information Technology / Queensland University of Technology / Both Positive and Negative Relevance Feedback QUT E-Discovery Lab / Asia University / /

Person

Features Let / Xiaohui Tao / /

Position

General / /

Product

Sigma DP1 Digital Camera / Sigma DP2 Digital Camera / P10A / TREC’09 Relevance Feedback / /

Technology

relevance feedback algorithms / Information Technology / API / same relevance feedback algorithm / relevance feedback algorithm / SPMining algorithm / /

URL

http /

SocialTag