Cong Long Yu

20110264651, Oct 27, 2011

Apr 21, 2010

12/764694

Sathiya K. Selvaraj - Cupertino CA, US
Philip L. Bohannon - Cupertino CA, US
Mridul Muralidharan - Bangalore, IN
Cong Yu - Hoboken NJ, US
Arun S. Iyer - Dombivli (W), IN
Sundararajan Sellamanickam - Bangalore, IN

YAHOO! INC. - Sunnyvale CA

G06F 17/30
G06F 15/18

707723, 707740, 706 12, 707E1709, 707754, 707E17005, 707E17014

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.

20120143875, Jun 7, 2012

Dec 1, 2010

12/958151

Anish Das Sarma - San Francisco CA, US
Alpa Jain - San Jose CA, US
Cong Yu - Hoboken NJ, US

G06F 17/30

707746, 707E17014

Method, system, and programs for detecting dynamic relationship and discovering dynamic events. Data from a first data source is first received. At least one dynamic relation candidate is identified and each dynamic relation candidate involves multiple entities. The at least one dynamic relation candidate is identified based on temporal properties with respect to the entities exhibited in the data from the first data source. Dynamic relations are then extracted by corroborating the temporal properties of the entities involved in the at least one dynamic relation candidate with that of the same entities exhibited in data from a second data source. Then, a dynamic event that gives rise to the dynamic relations among different entities is detected.

20120166428, Jun 28, 2012

Dec 22, 2010

12/975389

Vinay KAKADE - Sunnyvale CA, US
Raghu RAMAKRISHNAN - Santa Clara CA, US
Cong YU - Hoboken NJ, US

Yahoo! Inc - Sunnyvale CA

G06F 17/30

707723, 707E17014

A method of improving quality of web content. The method includes analyzing search logs associated with a plurality of web pages by a processor. The search logs are stored in an electronic storage device. A plurality of queries from the search logs are assembled into one or more query profiles. Concepts for the one or more query profiles are generated and classified into one or more concept profiles. Further, the one or more concept profiles are ranked based on one or more parameters. The one or more concept profiles are then transmitted to one or more mediums.

20220335221, Oct 20, 2022

Nov 21, 2019

17/778628

- Mountain View CA, US
Shan Jiang - Mountain View CA, US
Abraham P. Ittycheriah - Mountain View CA, US
Cong Yu - Mountain View CA, US

G06F 40/30
G06F 40/279

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate automatic log identification of a set of fact-check factors from digital documents. Digital documents can be identified from a plurality of sources. For each digital document, a set of fact check factors are identified using a trained sequence Fact Checking tagging model. Based on the sequence tagging model, a confidence value representing a likelihood that the set of fact check factors identified from the digital document are an actual set of fact check factors for the digital document is determined. The set of fact check factors is stored in association with the digital document. A request for fact check factors for a particular digital document among the digital documents is received from a fact checking entity. In response, the set of fact check factors identified from the particular digital document are provided to the fact checking entity.