Machine-learned approach to determining document relevance for search over large electronic collections of documents
US7287012B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 9, 2004 |
| Grant date | Oct 23, 2007 |
| Priority date | — |
| Expiry date | Sep 15, 2025 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/951
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.