The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 15, 2003

Filed:

Dec. 17, 1998
Applicant:
Inventors:

Sanjeev Katariya, Issaquah, WA (US);

William P. Jones, Kirkland, WA (US);

Assignee:

Microsoft Corporation, Redmond, WA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 1/730 ; G06F 1/500 ;
U.S. Cl.
CPC ...
G06F 1/730 ; G06F 1/500 ;
Abstract

A method and system for generating a weight for phrases within each document in a collection of documents. Each document has terms such as words and numbers. Each phrase comprises component terms. Each term frequency represents the number of occurrences of a term in a document, and the phrase frequency represents the number of occurrences of a phrase in a document. To generate the weight, the weighting system first estimates a document frequency for the phrase by multiplying an estimated phrase probability of the phrase times the number of documents that contain each component term. The estimated phrase probability is an estimation of the probability that any phrase in documents that contain each component term is the phrase whose weight is to be estimated. The document frequency is the number of the documents that contain the phrase. The weighting system then estimates a total phrase frequency for the phrase as the average phrase frequency for the phrase times the estimated document frequency for the phrase. The weighting system derives the average phrase frequency from the phrase probability of the phrase and average number of terms per document. The weighting system then combines the estimated document frequency with the estimated total phrase frequency to generate the weight of the phrase.


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