Google Inc.

Fei Wu

Alon Yitzchak Halevy

Jayant Madhavan

Shane Riley Brennan

Michael Greenberg

Bertrand Damiba

Olivia Grace

Yuan Gao

Faen Zhang

Cong Yu

Amit Behal

Keith Patrick Golden

Rahul Gupta

Steven Euijong Whang

Ben Hutchinson

Xiao Ling

Alexander Oliver Marks

The systems and methods described herein can generate a voice-based interface to increase the accuracy of translations. The voice-based interface can result in fewer input audio signals being transmitted between devices of a network. A method includes: receiving a first input audio signal; generating, based on the first input audio signal, a first translation string in a second language and a second translation string in a first language; determining a first translation score based on a likelihood that the first input audio signal includes an utterance in the first language and a second translation score based on a likelihood that the first input audio signal includes an utterance in the second language; selecting the first translation string based on the first translation score and the second translation score; generating an output signal from the first translation string; and transmitting the output signal to the client device.

Voice-based interface for translating utterances between users

The systems and methods described herein can generate a voice-based interface to increase the accuracy of translations. The voice-based interface can result in fewer input audio signals being transmitted between devices of a network. Reducing the number of redundant translation requests that are sent between the devices of a network can save bandwidth and other computational resources by processing fewer input audio signals.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an ontology of entity attributes. One of the methods includes extracting a plurality of attributes based upon a plurality of queries; and constructing an ontology based upon the plurality of attributes and a plurality of entity classes.

Identifying entity attributes

Systems and techniques are provided for generating a union table with from stitchable tables. Tables may be extracted from web pages to obtain extracted tables. Stitchable tables may be determined from the extracted tables. Hidden attributes for the stitchable tables may be extracted from the web pages from which the stitchable tables were extracted using segmentation of text for contextual data from the web pages into segment sequences, and alignment of the segment sequences. Iterative pairwise alignment may be used to align the segment sequences and obtain aligned segments. The stitchable tables may be joined into a union table. Hidden attributes from the aligned segments may be added to the union table. Headers for the hidden attributes in the union table may be labeled using a database of entities and class labels.

Synthesizing union tables from the web

A system for automatically generating entity collections comprises a data graph including entities connected by edges and instructions that cause the computer system to determine a set of entities from the data graph and to determine a set of constraints that has a quantity of constraints. A constraint in the set represents a path in the data graph shared by at least two of the entities in the set of entities. The instructions also cause the computer system to generate candidate collection definitions from combinations of the constraints, where each candidate collection definition identifies at least one constraint and no more than the quantity of constraints. The instructions also cause the computer system to determine an information gain for at least some of the candidate collection definitions, and store at least one candidate collection definition that has an information gain that meets a threshold as a candidate collection.

Automatic definition of entity collections

Methods, systems, and apparatus, including computer program products, for generating aspects associated with entities. In some implementations, a method includes receiving data identifying an entity; generating a group of candidate aspects for the entity; modifying the group of candidate aspects to generate a group of modified candidate aspects comprising combining similar candidate aspects and grouping candidate aspects using one or more aspect classes each associated with one or more candidate aspects; ranking one or more modified candidate aspects in the group of modified candidate aspects based on a diversity score and a popularity score; and storing an association between one or more highest ranked modified candidate aspects and the entity. The aspects can be used to organize and present search results in response to queries for the entity.

Growing community of inventors

Seattle, WA, United States of America

Fei Wu