Google Inc.

Slaven Bilac

Kavi J Goel

Hiroshi Ichikawa

Satoru Stephen Yamauchi

Yang-Hua Chu

Toshiaki Fujiki

Hidetoshi Shimokawa

Tomohiko Kimura

Toshihiro Yoshino

Mingmin Xie

A device may be configured to receive a query; receive information regarding documents that are relevant to the query; identify one or more entities associated with the documents; determine a category for the query based on: the query, a topic of the documents, and the one or more entities; determine, based on the query and the category, that an entity list should be presented in response to the query; and present a search result document based on determining that the entity list should be presented in response to the query. The search result document may include a list with information identifying the one or more entities.

Generating ranked lists of entities

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for ranking events. One of the methods includes receiving data identifying a physical location; obtaining data identifying a plurality of events occurring in the physical location during a particular time period; computing a respective plurality of signal scores for each of the events, wherein computing the respective plurality of signal scores for each of the events comprises computing a first signal score for each of the events based at least in part on a count of Internet sites that include at least one mention of the event; computing a respective popularity score for each of the plurality of events by combining the respective plurality of signal scores for the event; and generating a ranking of events occurring in the physical location during the particular time period based at least in part on the popularity scores.

Ranking events

Methods, systems, and apparatus, including computer program products, for automatically identifying transliteration pairs are disclosed. In one implementation, a method is provided. The method includes receiving a plurality of resources, the plurality of resources including a plurality of anchor text; determining one or more potential transliterations from the plurality of anchor text; and identifying one or more potential transliteration pairs from the one or more potential transliterations, where each potential transliteration pair includes a first anchor text in a first writing system and a second anchor text in a second writing system, the second anchor text and the first anchor text identifying a same resource or location.

Machine learning for transliteration

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Tokyo, Japan

Slaven Bilac