Google Inc.

Hui Tan

Maureen Heymans

Bahman Rabii

Gaofeng Zhao

Wei Chai

Yingwei Cui

Jeffrey Chin

Alexandre Kojoukhov

Vanja Josifovski

Jie Yang

Luis Garcia Pueyo

Mike Bendersky

MyLinh Yang

Amitabh Saikia

Marc-Allen Cartright

Jinan Lou

Sung-jung Cho

Yew Jin Lim

Marc Alexander Najork

Alexander Johannes Smola

Jocelyn C Lin

Sha-Mayn Teh

Xincheng Zhang

Amr Ahmed

Zhiyu Wang

Claire Cui

Weinan Zhang

Systems and methods of evaluating information in a computer network environment are provided. A data processing system can obtain or receive a content placement criterion, such as a keyword, associated with a content item and can determine a quality metric of the content placement criterion. The data processing system can identify a candidate content placement criterion and expand placement criteria associated with the content item to include the content placement criterion and the candidate content placement criterion based at least in part on an evaluation of the quality metric of the content placement criterion. The data processing system can expand placement criteria based in part on a throttling parameter. The data processing system can identify a correlation between a document and the placement criteria to identify appropriate content items for the document.

Expansion of high performing placement criteria

Methods, apparatus, and computer-readable media are provided for analyzing a cluster of communications, such as B2C emails, to generate a template for the cluster that defines transient segments and fixed segments of the cluster of communications. More particularly, methods, apparatus, and computer-readable media are provided for generating and/or applying a trained structured machine learning model for a generated template that can be used to determine, for one or more transient segments of subsequent communications, a corresponding probability that a given semantic label is the correct semantic label for extracted content of the transient segment(s).

Generating and applying a trained structured machine learning model for determining a semantic label for content of a transient segment of a communication

Techniques are described herein for generating and applying event data extraction templates. In various implementations, a data extraction template may be applied to structured communications to extract, from each structured communication, event data associated with a transient markup language path indicated in the data extraction template. The data extraction template may include an event-related semantic data type assigned to the transient markup language path and a strength of association between the transient structural path and the event-related semantic data type. Feedback may be obtained concerning event data extracted from one or more of the structured communications. Based on the feedback, the strength of association between the transient markup language path and the event-related semantic data type may be altered. The data extraction template may then be applied to a subsequent structured communication to extract new event data from the structured communication based on the altered strength of association.

Generating and applying event data extraction templates

Methods and apparatus related to clustering documents based on one or more classification terms and optionally based on similarity of structural paths of the documents. In some implementations, the documents are communications such as structured emails or other structured communications. In some of those implementations, clustering the communications includes identifying a plurality of classification terms indicative of a classification, identifying a corpus of communications that includes communications that are not labeled with an association to the classification, and determining a cluster of the communications based on occurrence of one or more of the classification terms in the communications of the cluster.

Clustering communications based on classification

Methods and apparatus are described herein for generating and applying event data extraction templates. In various implementations, a set of structural paths may be identified from a corpus of communications. A first structural path of the set of structural paths, associated with a first segment of text, may be classified as transient in response to a determination that a frequency of occurrences of the first segment of text across the corpus satisfies a criterion. Event heuristics may be applied to the communications of the corpus. A determination may be made, based on the applying, that the communications of the corpus are event-related. An event data type may be assigned to the transient structural path based on the applying. An event data extraction template may be generated to extract, from one or more subsequent communications, one or more event-related segments of text associated with the transient structural path.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for cross-language information retrieval. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving a query in a source language, the query including one or more query terms; automatically determining one or more target languages relevant to the query; translating the query into one or more translated queries in the respective one or more target languages; determining search results responsive to the respective one or more translated queries; and providing one or more of the search results.

Language selection for information retrieval

Methods, systems, and apparatus, including computer program products, for cross-language information retrieval. A source query in a source language is received. The source query is translated into a target query in a target language. One or more target-language resources that satisfy the target query are identified. The identified resources are translated into the source language. Search results corresponding to the translated resources are presented.

Cross-language information retrieval

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for cross-language information retrieval. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of identifying a structured collection of documents, the collection of documents being organized according to a hierarchy of categories; extracting entities from structured collection of document; assigning language scores to each document in the collection of documents; assigning language scores to entities based on scores of associated documents of the collection of documents; and generating a mapping between entities and language scores.

Methods, systems, and apparatus, including computer program products, in which a cross-language search can be performed. A search query is received in a first language. A translated search query of the search query is obtained and evaluated to determine whether it is a candidate for cross-language searching. Upon identifying a candidate, a search can be performed using the translated search query to generate search results relevant to the translated search query.

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Palo Alto, CA, United States of America

Hui Tan