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    • 3. 发明授权
    • Reverse question answering
    • 反问题回答
    • US09116996B1
    • 2015-08-25
    • US13557147
    • 2012-07-24
    • John R. ProvineAbhijit A. MahabalJohn J. Lee
    • John R. ProvineAbhijit A. MahabalJohn J. Lee
    • G06F17/30
    • G06F17/30867G06F17/30675
    • Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for associating each of a plurality of different terms to documents that contain the term to create first associations; associating each of the associated documents to one or more queries to create second associations, wherein search results for each of the queries include a reference to the associated document; determining a particular query and a particular term are associated with a same document based on the first and second associations; in response to the determination, creating a mapping between the particular query and the particular term when both are associated with the same document; and selecting a respective answer from a plurality of mapped terms for each the mapped queries, wherein the respective answer is selected based on a count the respective answer occurs in documents contained in search results for the mapped queries.
    • 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,用于将多个不同术语中的每一个与包含术语的文档相关联以创建第一关联; 将每个相关联的文档与一个或多个查询相关联以创建第二关联,其中每个查询的搜索结果包括对相关联文档的引用; 基于第一和第二关联,确定特定查询和特定术语与相同文档相关联; 响应于该确定,当两者都与相同文档相关联时,创建特定查询和特定术语之间的映射; 以及针对每个所述映射的查询从多个映射项中选择相应的答案,其中,基于所映射查询的搜索结果中包含的文档中出现相应答案的计数来选择相应的答案。
    • 5. 发明授权
    • Learning expected values for facts
    • 学习事实的预期价值
    • US08560468B1
    • 2013-10-15
    • US13025117
    • 2011-02-10
    • Kevin LermanVinicius J. FortunaAndrew W. HogueJohn R. ProvineEngin Cinar SahinJohn J. Lee
    • Kevin LermanVinicius J. FortunaAndrew W. HogueJohn R. ProvineEngin Cinar SahinJohn J. Lee
    • G06F15/18
    • G06N5/02
    • Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for machine learning. In one aspect, a method includes receiving a collection of facts, each fact represented as an entity-attribute-value tuple; identifying expected values for one or more individual attributes, where the identifying expected values includes, for each particular attribute: identifying facts having the attribute, calculating a value score for facts of the collection of facts having the particular attribute for each particular value, calculating a global score for all facts of the collection having the attribute, and comparing the value score to the global score such that a value is identified as an expected value if the comparison satisfies a specified threshold.
    • 方法,系统和装置,包括在计算机存储介质上编码的用于机器学习的计算机程序。 一方面,一种方法包括接收事实的集合,每个事实表示为实体属性值元组; 识别对于每个特定属性的识别期望值包括的一个或多个个体属性的期望值,识别具有该属性的事实,为每个特定值具有特定属性的事实的收集事实计算事实的值得分,计算 具有所述属性的所述集合的所有事实的全局得分,并且将所述值得分与所述全局得分进行比较,使得如果所述比较满足指定阈值,则将该值识别为期望值。