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    • 3. 发明申请
    • PROVIDING APP STORE SEARCH RESULTS
    • 提供APP存储搜索结果
    • US20160299972A1
    • 2016-10-13
    • US15092459
    • 2016-04-06
    • Google Inc.
    • Rajhans SamdaniAmarnag SubramanyaFernando PereiraHrishikesh Aradhye
    • G06F17/30G06F17/22H04L29/08
    • G06F16/951G06F16/24534G06F16/24573G06F16/95G06F17/2235H04L67/02
    • 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.
    • 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。
    • 4. 发明授权
    • Training a natural language processing model with information retrieval model annotations
    • 培训具有信息检索模型注释的自然语言处理模型
    • US09536522B1
    • 2017-01-03
    • US14143011
    • 2013-12-30
    • Google Inc.
    • Keith HallDipanjan DasTerry Yang-Hoe KooFernando Pereira
    • G10L15/18G10L15/22G10L15/06G06F17/30
    • G10L15/18G06F17/3066G06F17/30684G06N5/00G10L15/063G10L15/1822
    • 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.
    • 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。
    • 8. 发明授权
    • Automatic annotation for training and evaluation of semantic analysis engines
    • 自动注释用于语义分析引擎的训练和评估
    • US09224103B1
    • 2015-12-29
    • US13801197
    • 2013-03-13
    • Google Inc.
    • Amarnag SubramanyaFernando Pereira
    • G06F15/18G06N99/00
    • G06N99/005
    • 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.
    • 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。