Machine-learned approach to determining document relevance for search over large electronic collections of documents
Number of patents in Portfolio can not be more than 2000
United States of America Patent
Stats
-
Oct 23, 2007
Grant Date -
Jul 14, 2005
app pub date -
Jan 9, 2004
filing date -
Jan 9, 2004
priority date (Note) -
In Force
status (Latency Note)
Importance
Abstract
The present invention relates to a system and methodology that applies automated learning procedures for determining document relevance and assisting information retrieval activities. A system is provided that facilitates a machine-learned approach to determine document relevance. The system includes a storage component that receives a set of human selected items to be employed as positive test cases of highly relevant documents. A training component trains at least one classifier with the human selected items as positive test cases and one or more other items as negative test cases in order to provide a query-independent model, wherein the other items can be selected by a statistical search, for example. Also, the trained classifier can be employed to aid an individual in identifying and selecting new positive cases or utilized to filter or re-rank results from a statistical-based search.
First Claim
Family
International Classification(s)
Cited Art Landscape
Patent Citation Ranking
Advertisement
Advertisement
Advertisement
Recipient Email Address
Recipient Email Address
Comment
Recipient Email Address
Add to Portfolio(s)
To add this patent to one, or more, of your portfolios, simply click the add button.
This Patent is in these Portfolios:
Add to additional portfolios:
Last Refreshed On:
Changes done successfully