公开(公告)号：US08923655B1

公开(公告)日：2014-12-30

申请号：US13650489

申请日：2012-10-12

Applicant: Google Inc.

Inventor： Jason Weston ,  Samy Bengio

IPC: G06K9/60

CPC classification number: G06F17/30274

Abstract: A server device determines a plurality of images for a query. One or more images, of the plurality of images, are associated with one or more senses of the query. The server device maps the plurality of images into a space by representing the plurality of images with corresponding points in the space; determines one or more hyperplanes in the space based on the corresponding points in the space; calculates one or more scores for the plurality of images based on the corresponding points and the one or more hyperplanes; and ranks the one or more images based on the one or more scores.

Abstract translation: 服务器设备确定用于查询的多个图像。 多个图像中的一个或多个图像与查询的一个或多个感觉相关联。 服务器设备通过用空间中的相应点表示多个图像来将多个图像映射到空间中; 基于空间中的对应点确定空间中的一个或多个超平面; 基于对应点和一个或多个超平面来计算多个图像的一个或多个分数; 并根据一个或多个分数对一个或多个图像进行排序。

公开(公告)号：US09262406B1

公开(公告)日：2016-02-16

申请号：US14271997

申请日：2014-05-07

Applicant: Google Inc.

Inventor： Dipanjan Das ,  Kuzman Ganchev ,  Jason Weston ,  Karl Moritz Hermann

IPC: G06F17/28 ,  G06F17/27

CPC classification number: G06N5/027 ,  G06F17/2264 ,  G06F17/274 ,  G06F17/2755 ,  G06F17/2785 ,  G06F17/2872 ,  G06F17/289 ,  G06F17/30625 ,  G06F17/30631 ,  G06F17/3089

Abstract: A computer-implemented technique can include receiving, at a server, labeled training data including a plurality of groups of words, each group of words having a predicate word, each word having generic word embeddings. The technique can include extracting, at the server, the plurality of groups of words in a syntactic context of their predicate words. The technique can include concatenating, at the server, the generic word embeddings to create a high dimensional vector space representing features for each word. The technique can include obtaining, at the server, a model having a learned mapping from the high dimensional vector space to a low dimensional vector space and learned embeddings for each possible semantic frame in the low dimensional vector space. The technique can also include outputting, by the server, the model for storage, the model being configured to identify a specific semantic frame for an input.

Abstract translation: 计算机实现的技术可以包括在服务器处接收包括多组单词的标记训练数据，每组单词具有谓词单词，每个单词具有通用单词嵌入。 该技术可以包括在服务器处提取他们的谓词单词的句法语境中的多组单词。 该技术可以包括在服务器处连接通用词嵌入以创建表示每个单词的特征的高维向量空间。 该技术可以包括在服务器处获得具有从高维矢量空间到低维向量空间的学习映射的模型，以及在低维向量空间中为每个可能的语义帧学习嵌入。 该技术还可以包括由服务器输出用于存储的模型，该模型被配置为识别用于输入的特定语义帧。

公开(公告)号：US20160239739A1

公开(公告)日：2016-08-18

申请号：US15008794

申请日：2016-01-28

Applicant: Google Inc.

Inventor： Dipanjan Das ,  Kuzman Ganchev ,  Jason Weston ,  Karl Moritz Hermann

IPC: G06N5/02 ,  G06F17/22 ,  G06F17/28 ,  G06F17/30

CPC classification number: G06N5/027 ,  G06F17/2264 ,  G06F17/274 ,  G06F17/2755 ,  G06F17/2785 ,  G06F17/2872 ,  G06F17/289 ,  G06F17/30625 ,  G06F17/30631 ,  G06F17/3089

Abstract: A computer-implemented technique can include receiving, at a server, labeled training data including a plurality of groups of words, each group of words having a predicate word, each word having generic word embeddings. The technique can include extracting, at the server, the plurality of groups of words in a syntactic context of their predicate words. The technique can include concatenating, at the server, the generic word embeddings to create a high dimensional vector space representing features for each word. The technique can include obtaining, at the server, a model having a learned mapping from the high dimensional vector space to a low dimensional vector space and learned embeddings for each possible semantic frame in the low dimensional vector space. The technique can also include outputting, by the server, the model for storage, the model being configured to identify a specific semantic frame for an input.

Abstract translation: 计算机实现的技术可以包括在服务器处接收包括多组单词的标记训练数据，每组单词具有谓词单词，每个单词具有通用单词嵌入。 该技术可以包括在服务器处提取他们的谓词单词的句法语境中的多组单词。 该技术可以包括在服务器处连接通用词嵌入以创建表示每个单词的特征的高维向量空间。 该技术可以包括在服务器处获得具有从高维矢量空间到低维向量空间的学习映射的模型，以及在低维向量空间中为每个可能的语义帧学习嵌入。 该技术还可以包括由服务器输出用于存储的模型，该模型被配置为识别用于输入的特定语义帧。