Google Inc.

John Burge

George Dan Toderici

Omid Madani

Sanketh Shetty

Weilong Yang

Balakrishnan Varadarajan

Apostol Ivanov Natsev

Clyde D McQueen

Michael Skinner

Aaron Shon

Matthew Zitzmann

Facilitating of content entity annotation while maintaining joint quality, coverage and/or completeness performance conditions is provided. In one example, a non-transitory computer-readable medium comprises computer-readable instructions that, in response to execution, cause a computing system to perform operations. The operations include aggregating information indicative of initial entities for content and initial scores associated with the initial entities received from one or more content annotation sources and mapping the initial scores to respective values to generate calibrated scores. The operations include applying weights to the calibrated scores to generate weighted scores and combining the weighted scores using a linear aggregation model to generate a final score. The operations include determining whether to annotate the content with at least one of the initial entities based on a comparison of the final score and a defined threshold value.

Facilitating content entity annotation while satisfying joint performance conditions

Facilitation of content entity annotation while maintaining joint quality, coverage and/or completeness performance conditions is provided. In one example, a system includes an aggregation component that aggregates signals indicative of initial entities for content and initial scores associated with the initial entities generated by one or more content annotation sources; and a mapping component that maps the initial scores to calibrated scores within a defined range. The system also includes a linear aggregation component that: applies selected weights to the calibrated scores, wherein the selected weights are based on joint performance conditions; and combines the weighted, calibrated scores based on a selected linear aggregation model of a plurality of linear aggregation models to generate a final score. The system also includes an annotation component that determines whether to annotate the content with one of the initial entities based on a comparison of the final score and a defined threshold value.

A resource context system ('context system') computes a contextual profile for a resource based on selection data for the resource and contextual profiles of other resources that are identified as relevant to the resource. The contextual profile includes values that specify measures of relevance of the resource to each of a plurality of corresponding topics. The contextual profile is provided to a processing system, such as an advertisement management system or a search system that can identify topics for which the resource is relevant based on the contextual profile.

Context based resource relevance

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Santa Clara, CA, United States of America

John Burge