公开(公告)号：US09146967B2

公开(公告)日：2015-09-29

申请号：US13851036

申请日：2013-03-26

Applicant: Google Inc.

Inventor： Jeffrey A. Dean ,  Paul G. Haahr ,  Olcan Sercinoglu ,  Amitabh K. Singhal

IPC: G06F17/00 ,  G06F17/30

CPC classification number: G06F17/3053 ,  G06F17/3061 ,  G06F17/30864

Abstract: A multi-stage query processing system and method enables multi-stage query scoring, including “snippet” generation, through incremental document reconstruction facilitated by a multi-tiered mapping scheme. At one or more stages of a multi-stage query processing system a set of relevancy scores are used to select a subset of documents for presentation as an ordered list to a user. The set of relevancy scores can be derived in part from one or more sets of relevancy scores determined in prior stages of the multi-stage query processing system. In some embodiments, the multi-stage query processing system is capable of executing one or more passes on a user query, and using information from each pass to expand the user query for use in a subsequent pass to improve the relevancy of documents in the ordered list.

Abstract translation: 多级查询处理系统和方法通过多层次映射方案促进的增量文档重建实现了多阶段查询评分，包括“代码段”生成。 在多阶段查询处理系统的一个或多个阶段，使用一组相关性分数来选择文档的子集，以作为用户的排序列表呈现。 相关性分数的集合可以部分地从多级查询处理系统的先前阶段中确定的一组或多组相关性得分导出。 在一些实施例中，多级查询处理系统能够执行用户查询的一个或多个传递，并且使用来自每个遍的信息来扩展用户查询以用于随后的传递中以改善订购中的文档的相关性 列表。

公开(公告)号：US09141916B1

公开(公告)日：2015-09-22

申请号：US13803779

申请日：2013-03-14

Applicant: Google Inc.

Inventor： Gregory S. Corrado ,  Kai Chen ,  Jeffrey A. Dean ,  Gary R. Holt ,  Julian P. Grady ,  Sharat Chikkerur ,  David W. Sculley

IPC: G06F15/18 ,  G06N99/00

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/084

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于使用具有深度网络的嵌入式功能。 方法之一包括接收包括多个特征的输入，其中每个特征具有不同的特征类型; 使用相应的嵌入功能处理每个特征以生成一个或多个数值，其中每个嵌入功能独立于彼此嵌入功能操作，并且其中每个嵌入功能用于相应特征类型的特征; 使用深度网络处理所述数值以产生所述输入的第一替代表示，其中所述深度网络是由多个非线性操作级别组成的机器学习模型; 以及使用逻辑回归分类器处理输入的第一替代表示以预测输入的标签。

公开(公告)号：US09141906B2

公开(公告)日：2015-09-22

申请号：US13802184

申请日：2013-03-13

Applicant: Google Inc.

Inventor： Kai Chen ,  Xiaodan Song ,  Gregory S. Corrado ,  Kun Zhang ,  Jeffrey A. Dean ,  Bahman Rabii

IPC: G06F15/18 ,  G06E1/00 ,  G06E3/00 ,  G06G7/00 ,  G06N3/08 ,  G06F17/30 ,  G06N3/04 ,  G06Q30/02

CPC classification number: G06N3/084 ,  G06F17/30707 ,  G06F17/30864 ,  G06N3/04 ,  G06N3/0427 ,  G06N3/08 ,  G06Q30/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values to generate an alternative representation of the features of the resource, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input to generate a respective relevance score for each concept term in a pre-determined set of concept terms, wherein each of the respective relevance scores measures a predicted relevance of the corresponding concept term to the resource.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于使用深层网络评分概念术语。 所述方法之一包括接收包括资源的多个特征的输入，其中每个特征是所述资源的相应属性的值; 使用相应的嵌入功能处理每个特征以生成一个或多个数值; 处理所述数值以产生所述资源的特征的替代表示，其中处理所述浮点值包括将一个或多个非线性变换应用于所述浮点值; 以及处理所述输入的替代表示以在预定概念术语集合中为每个概念项产生相应的相关性得分，其中各个相关性分数中的每一个测量相应概念项与所述资源的预测相关性。

公开(公告)号：US20150169578A1

公开(公告)日：2015-06-18

申请号：US13928868

申请日：2013-06-27

Applicant: GOOGLE INC.

Inventor： Emanuel Taropa ,  Jeffrey A. Dean ,  Simon Tong ,  Nitin Gupta ,  Noam M. Shazeer ,  Anwis Das ,  Murtaza A. Basrai

IPC: G06F17/30

CPC classification number: G06F17/3097 ,  G06F17/3064 ,  G06F17/30646 ,  G06F17/30672

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reranking query completions based on activity session data. One of the methods includes receiving a query prefix from a user. Query completions are obtained for the query prefix. One or more likely queries that are likely to co-occur with a reference query in user activity sessions are obtained. If one of the likely queries matches one of the query completions, a modified ranking of the query completions is determined, including boosting a ranking of matching query completions. The modified ranking of the query completions is provided in response to receiving the query prefix.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于基于活动会话数据重新排列查询完成。 其中一种方法包括从用户接收查询前缀。 获取查询前缀的查询完成。 获得在用户活动会话中可能与参考查询共存的一个或多个可能的查询。 如果一个可能的查询与其中一个查询完成相匹配，则确定查询完成的修改排名，包括提升匹配查询完成的排名。 响应于接收查询前缀而提供查询完成的修改排名。

公开(公告)号：US09037464B1

公开(公告)日：2015-05-19

申请号：US13841640

申请日：2013-03-15

Applicant: Google Inc.

Inventor： Tomas Mikolov ,  Kai Chen ,  Gregory S. Corrado ,  Jeffrey A. Dean

IPC: G10L15/00 ,  G06F17/28

CPC classification number: G06F17/2765 ,  G06F17/2785 ,  G06N99/005 ,  G10L15/06

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于计算单词的数字表示。 一种方法包括获得一组训练数据，其中训练数据集合包括单词序列; 在训练数据集上训练分类器和嵌入函数，其中训练嵌入函数包括获得的嵌入函数参数的训练值; 使用嵌入函数根据嵌入函数参数的训练值来处理词汇表中的每个单词以产生高维空间中词汇表中每个单词的相应数值表示; 并将词汇表中的每个单词与高维空间中单词的相应数字表示相关联。