Yahoo! Inc.

Oath Inc.

Yahoo Holdings, Inc.

Mridul Muralidharan

Lluis Garcia Pueyo

Roelof Van Zwol

Lyndon Kennedy

Yash Dayal

Thomas Gulik

Tejaswi Kasturi

Philip Lewis Bohannon

Cong Yu

Arun Sundaresan Iyer

Sathiya Keerthi Selvaraj

Sundararajan Sellamanickam

Ashwin Machanavajjhala

Disclosed herein are aspects associated with contextual, or related, media enrichment presentation item of a media object served via the internet. A request to annotate a media object in connection with the media object's presentation is received, and a media object identifier and a profile identifier are obtained. The media object's information is retrieved using the media object identifier, and a profile is retrieved using the profile identifier. A response including one or more references to one or more media enrichment presentation items is transmitted, each reference to a media enrichment presentation item comprising information for use in retrieving the media enrichment presentation item for presentation in connection with presentation of the media object.

Media enrichment system and method

A system and method is described for large scale entity-specific classification of each entity-specific set of candidates in a collection of candidates for each specific entity in a collection of entities. The collection of entities may comprise a specific category or domain of entities (e.g. schools, restaurants, manufacturers, products, events, people). Candidates may comprise webpages or other resources with resource identifiers. Entity specific sets of candidates may be found by leveraging search engine query results and user interaction therewith for queries based on entity-specific attributes. The relationship(s) or class(es) for which candidate resources are being classified relative to a specific entity may comprise an authoritative, official home page (OHP), or other class (e.g. fan page, review, aggregator) relative to a specific entity. A feature generator generates entity-specific features for candidates. In accordance with its features, one or more classifiers rank each candidate for a specific class for a specific entity.

Large scale entity-specific resource classification

Example methods, apparatuses, or articles of manufacture are disclosed that may be implemented using one or more computing devices to facilitate or otherwise support one or more processes or operations associated with ranking entity facets using user-click feedback.

Ranking entity facets using user-click feedback

After receipt of a training and execution plan, a trainer operator is automatically trained based on specified training documents so as to generate a new trained operator for extracting information from documents. The new trained operator is a new version of the trainee operator. Both trainee operators are automatically retained for later use in extracting information from one or more unknown documents. After receipt of the training and execution plan, the new trained operator is automatically executed on one or more unknown documents so as to extract information from such one or more unknown documents.

Apparatus and methods for operator training in information extraction

Growing community of inventors

Bengaluru, India

Mridul Muralidharan