Google Inc.

Simon Tong

Paul Haahr

Noam M Shazeer

Jeffrey Adgate Dean

Karl Pfleger

Steve Lawrence

Georges R Harik

Monika Hildegard Henzinger

Uri N Lerner

Olcan Sercinoglu

Amit Singhal

Joshua L Levenberg

Jeremy Bem

Steven D Baker

Anurag Acharya

Hyung-Jin Kim

Matt Cutts

Michelangelo Diligenti

Sergey Brin

John Piscitello

David Braginsky

Boris Mazniker

Matthieu Devin

Lawrence Jonathan Brunsman

Monika Henzinger

Michael Angelo

Jeremy Ginsberg

David R Cheng

Sanjay Ghemawat

John Ogden Lamping

Benedict A Gomes

Sachin Jain

Shimrit Ben-Yair

Xuefu Wang

Vineet Gupta

Benjamin N Lee

Jeff Dean

Mizuki McGrath

Eric E Altendorf

Breen Hagan

Benjamin Tauber

Rishabh Singh

Amitabh Singhal

Vishnu Sivaji

Scott S Benson

Aaron Saul Zemach

Alexandre Petcherski

Michihiro Kuramochi

Chiraag Galaiya

Dima Brezhnev

David Lick

Francisco Velasquez

Max Lin

Neha Bhargava

Peilun Zhang

Rahul Srinivasan

Victoria Taylor

Zifan Xiao

Nitin Kumar Gupta

Mark E Pearson

Anwis Das

Emanuel Taropa

Urs Hoelzle

Junbin Teng

Ben Gomes

Murtaza A Basrai

Mizuki Mcgrath

Techniques are described herein for automatically synthesizing programs that include one or more functions in a spreadsheet programming language. A method includes: receiving a first example including input provided in a first cell in a spreadsheet; automatically synthesizing a plurality of candidate programs including a first set of candidate programs consistent with the first example, wherein each candidate program in the first set of candidate programs comprises at least one function in a spreadsheet programming language and, when the candidate program is executed, the candidate program generates output that matches the first example; ranking the plurality of candidate programs; and storing a highest-ranked program of the plurality of candidate programs in association with the first cell in the spreadsheet.

Synthesizing programs in a spreadsheet programming language

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for correcting entity names. One method includes receiving texts and deriving a plurality of name-context pairs from the texts. The method further includes calculating a context consistency measure for each name-context pair and storing context-entity name data representing the name-context pairs. Another method includes identifying an entity name and one or more context terms from a query and generating candidate names for the entity name. The method further includes determining a score for each of the candidate names, selecting a number of top scoring candidate names, and using the selected candidate names to respond to the query.

Generating context-based spell corrections of entity names

The present disclosure includes systems and techniques relating to ranking search results of a search query. In general, the subject matter described in this specification can be embodied in a computer-implemented method that includes determining a measure of relevance for a document result within a context of a search query for which the document result is returned, the determining being based on a first number in relation to a second number, the first number corresponding to longer views of the document result, and the second number corresponding to at least shorter views of the document result; and outputting the measure of relevance to a ranking engine for ranking of search results, including the document result, for a new search corresponding to the search query. The subject matter described in this specification can also be embodied in various corresponding computer program products, apparatus and systems.

Modifying search result ranking based on implicit user feedback

Methods, systems, and apparatus, including computer programs encoded on computer storage medium, for identifying a set of items of digital content displayed to a user; processing the set of items to identify a set of boost items, items within the set of boost items to be prominently displayed, processing comprising: receiving a close-ties score associated with a respective item, the close-ties score representing a relationship between the user and other users associated with the respective item and an importance of a social circle associated with the item to the user, determining that the close-ties score associated with the respective item exceeds a threshold close-ties score, and in response to determining that the close-ties score exceeds the threshold close-ties score, adding the respective item to the set of boost items; providing instructions for boosting a display of items in the set of boost items in a page displayed to the user.

Scoring content based on social interaction

A stopword detection component detects stopwords (also stop-phrases) in search queries input to keyword-based information retrieval systems. Potential stopwords are initially identified by comparing the terms in the search query to a list of known stopwords. Context data is then retrieved based on the search query and the identified stopwords. In one implementation, the context data includes documents retrieved from a document index. In another implementation, the context data includes categories relevant to the search query. Sets of retrieved context data are compared to one another to determine if they are substantially similar. If the sets of context data are substantially similar, this fact may be used to infer that the removal of the potential stopword(s) is not material to the search. If the sets of context data are not substantially similar, the potential stopword can be considered material to the search and should not be removed from the query.

Locating meaningful stopwords or stop-phrases in keyword-based retrieval systems

A system ranks documents based, at least in part, on a ranking model. The ranking model may be generated to predict the likelihood that a document will be selected. The system may receive a search query and identify documents relating to the search query. The system may then rank the documents based, at least in part, on the ranking model and form search results for the search query from the ranked documents.

Ranking documents based on large data sets

Methods and systems allow users to enter natural language terms that describe a particular web site into an address field of a browser instead of a formal URL. The terms are evaluated to determine whether they correspond, with a high likelihood, to a particular web site. If so, this web site may be immediately accessed. If not, a list of search results based on the terms may be displayed by the browser.

Named URL entry

Different ad selection techniques may be evaluated and compared by (i) combining ads generated using at least two different techniques and (ii) determining performance statistics of these combined (e.g., interleaved) advertising serves over time. The relative performance of the different techniques can then be determined. These principles can also be used to gauge different ad scoring techniques. These principles can also be used to gauge different ad rendering techniques.

Determining better ad selection, scoring, and/or presentation techniques

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Mountain View, CA, United States of America

Simon Tong