公开(公告)号：US10810193B1

公开(公告)日：2020-10-20

申请号：US13801598

申请日：2013-03-13

Applicant: Google Inc.

Inventor： Amarnag Subramanya ,  Fernando Pereira ,  Ni Lao ,  John Blitzer ,  Rahul Gupta

IPC: G06F16/245

Abstract: Implementations include systems and methods for querying a data graph. An example method includes receiving a machine learning module trained to produce a model with multiple features for a query, each feature representing a path in a data graph. The method also includes receiving a search query that includes a first search term, mapping the search query to the query, and mapping the first search term to a first entity in the data graph. The method may also include identifying a second entity in the data graph using the first entity and at least one of the multiple weighted features, and providing information relating to the second entity in a response to the search query. Some implementations may also include training the machine learning module by, for example, generating positive and negative training examples from an answer to a query.

公开(公告)号：US08666749B1

公开(公告)日：2014-03-04

申请号：US13744222

申请日：2013-01-17

Applicant: Google Inc.

Inventor： Amarnag Subramanya ,  Jennifer Gillenwater ,  Garth Griffin ,  Fernando Pereira ,  Douglas Eck

IPC: G10L11/00

CPC classification number: G06F17/30743

Abstract: The disclosure includes a system and method for generating audio snippets from a subset of audio tracks. In some embodiments an audio snippet is an audio summary of a group or collection of songs.

Abstract translation: 本公开包括用于从音频子集的子集产生音频片段的系统和方法。 在一些实施例中，音频片段是组或歌曲集合的音频摘要。

公开(公告)号：US20160299972A1

公开(公告)日：2016-10-13

申请号：US15092459

申请日：2016-04-06

Applicant: Google Inc.

Inventor： Rajhans Samdani ,  Amarnag Subramanya ,  Fernando Pereira ,  Hrishikesh Aradhye

IPC: G06F17/30 ,  G06F17/22 ,  H04L29/08

CPC classification number: G06F16/951 ,  G06F16/24534 ,  G06F16/24573 ,  G06F16/95 ,  G06F17/2235 ,  H04L67/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing app store search results. An example method includes responsive to a first search query directed to an app store: revising the first search query to produce a second search query different from the first search query; obtaining, from an Internet search engine, second search results responsive to the second search query; analyzing the second search results to identify apps available on the app store that are relevant to the second search query; obtaining, from the app store, first search results responsive to the first search query that identify apps available in the app store; and modifying the first search results based on analyzing the second search results.

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，用于提供应用商店搜索结果。 示例性方法包括响应于针对应用商店的第一搜索查询：修改第一搜索查询以产生与第一搜索查询不同的第二搜索查询; 从互联网搜索引擎获得响应于所述第二搜索查询的第二搜索结果; 分析第二搜索结果以识别应用商店上可用于与第二搜索查询相关的应用; 从应用商店获得响应于识别应用商店中可用的应用的第一搜索查询的第一搜索结果; 以及基于分析所述第二搜索结果来修改所述第一搜索结果。

公开(公告)号：US09536522B1

公开(公告)日：2017-01-03

申请号：US14143011

申请日：2013-12-30

Applicant: Google Inc.

Inventor： Keith Hall ,  Dipanjan Das ,  Terry Yang-Hoe Koo ,  Fernando Pereira

IPC: G10L15/18 ,  G10L15/22 ,  G10L15/06 ,  G06F17/30

CPC classification number: G10L15/18 ,  G06F17/3066 ,  G06F17/30684 ,  G06N5/00 ,  G10L15/063 ,  G10L15/1822

Abstract: Systems and techniques are provided for training a natural language processing model with information retrieval model annotations. A natural language processing model may be trained, through machine learning, using training examples that include part-of-speech tagging and annotations added by an information retrieval model. The natural language processing model may generate part-of-speech, parse-tree, beginning, inside, and outside label, mention chunking, and named-entity recognition predictions with confidence scores for text in the training examples. The information retrieval model annotations and part-of-speech tagging in the training example may be used to determine the accuracy of the predictions, and the natural language processing model may be adjusted. After training, the natural language processing model may be used to make predictions for novel input, such as search queries and potential search results. The search queries and potential search results may have information retrieval model annotations.

Abstract translation: 提供系统和技术，用于训练具有信息检索模型注释的自然语言处理模型。 可以通过机器学习，使用包括由信息检索模型添加的词性标注和注释的训练样本来训练自然语言处理模型。 自然语言处理模型可以生成词性，解析树，开始，内部和外部标签，提及分组和命名实体识别预测，在训练示例中具有文本的置信度分数。 可以使用训练示例中的信息检索模型注释和词性标签来确定预测的准确性，并且可以调整自然语言处理模型。 训练后，自然语言处理模型可用于对新颖的输入进行预测，如搜索查询和潜在搜索结果。 搜索查询和潜在搜索结果可能具有信息检索模型注释。

公开(公告)号：US09405849B1

公开(公告)日：2016-08-02

申请号：US15084166

申请日：2016-03-29

Applicant: Google Inc.

Inventor： Jakob D. Uszkoreit ,  John Blitzer ,  Engin Cinar Sahin ,  Rahul Gupta ,  Dekang Lin ,  Fernando Pereira ,  Ciprian I. Chelba

IPC: G06F17/00 ,  G06F17/30

CPC classification number: G06F17/30899 ,  G06F17/2705

Abstract: A method identifies pairs of first and second command inputs from respective user device sessions for which the first and second operation data are indicative of a first operation failure and a second operation success. The first operation data indicate a first operation performed on data from a first resource property in response to the first command input, and the second operation data indicate a second operation performed on data from a second resource property in response to the second command input. They system determines, from the identified pairs of first and second command inputs, command inputs for which a parsing rule that is associated with the second operation is to be generated.

Abstract translation: 一种方法识别来自相应用户设备会话的第一和第二命令输入对，其中第一和第二操作数据表示第一操作失败和第二操作成功。 第一操作数据指示响应于第一命令输入对来自第一资源属性的数据执行的第一操作，并且第二操作数据响应于第二命令输入指示针对来自第二资源属性的数据执行的第二操作。 它们系统从所识别的第一和第二命令输入对确定要生成与第二操作相关联的解析规则的命令输入。

公开(公告)号：US09330195B1

公开(公告)日：2016-05-03

申请号：US13926836

申请日：2013-06-25

Applicant: Google Inc.

Inventor： Jakob D. Uszkoreit ,  John Blitzer ,  Engin Cinar Sahin ,  Rahul Gupta ,  Dekang Lin ,  Fernando Pereira ,  Ciprian I. Chelba

IPC: G06F17/00 ,  G06F17/30

CPC classification number: G06F17/30899 ,  G06F17/2705

Abstract: A method identifies pairs of first and second command inputs from respective user device sessions for which the first and second operation data are indicative of a first operation failure and a second operation success. The first operation data indicate a first operation performed on data from a first resource property in response to the first command input, and the second operation data indicate a second operation performed on data from a second resource property in response to the second command input. They system determines, from the identified pairs of first and second command inputs, command inputs for which a parsing rule that is associated with the second operation is to be generated.

公开(公告)号：US09324323B1

公开(公告)日：2016-04-26

申请号：US13715139

申请日：2012-12-14

Applicant: Google Inc.

Inventor： Daniel M. Bikel ,  Kapil R. Thadini ,  Fernando Pereira ,  Maria Shugrina ,  Fadi Biadsy

IPC: G10L15/26 ,  G10L15/183 ,  G10L15/197

CPC classification number: G10L15/183 ,  G10L15/197

Abstract: Speech recognition techniques may include: receiving audio; identifying one or more topics associated with audio; identifying language models in a topic space that correspond to the one or more topics, where the language models are identified based on proximity of a representation of the audio to representations of other audio in the topic space; using the language models to generate recognition candidates for the audio, where the recognition candidates have scores associated therewith that are indicative of a likelihood of a recognition candidate matching the audio; and selecting a recognition candidate for the audio based on the scores.

Abstract translation: 语音识别技术可以包括：接收音频; 识别与音频相关联的一个或多个主题; 识别对应于所述一个或多个主题的主题空间中的语言模型，其中基于所述音频的表示与所述主题空间中的其他音频的表示的接近度来识别所述语言模型; 使用语言模型来生成用于音频的识别候选，其中识别候选具有与之相关联的分数，其指示与音频匹配的识别候选者的可能性; 以及基于分数来选择音频的识别候选。

公开(公告)号：US09224103B1

公开(公告)日：2015-12-29

申请号：US13801197

申请日：2013-03-13

Applicant: Google Inc.

Inventor： Amarnag Subramanya ,  Fernando Pereira

IPC: G06F15/18 ,  G06N99/00

CPC classification number: G06N99/005

Abstract: Implementations include systems and methods generate data for training or evaluating semantic analysis engines. For example, a method may include receiving documents from a corpus that includes an authoritative set of documents from an authoritative source. Each document in the authoritative set may be associated with an entity. A second set of documents from the corpus that do not overlap with the first set may include at least one link to a document in the authoritative set, the at least one link being associated with anchor text. For each document in the second set, the method may include identifying entity mentions in the document based on the anchor text. The method may include associating the entity mention with the entity in a graph-structured knowledge base or associating entity types with the entity mention. The method may also include training a semantic analysis engine using the identified entity mentions and associations.

Abstract translation: 实现包括系统和方法生成用于训练或评估语义分析引擎的数据。 例如，一种方法可以包括从语料库接收包括来自权威来源的权威的一组文件的文档。 权威集中的每个文档可能与一个实体相关联。 来自语料库的与第一组不重叠的第二组文档可以包括至少一个链接到权威集合中的文档，该至少一个链接与锚文本相关联。 对于第二组中的每个文档，该方法可以包括基于锚文本识别文档中的实体提及。 该方法可以包括将实体提及与图形结构化知识库中的实体相关联或将实体类型与实体提及相关联。 该方法还可以包括使用所识别的实体提及和关联来训练语义分析引擎。