公开(公告)号：US09020865B2

公开(公告)日：2015-04-28

申请号：US14186826

申请日：2014-02-21

Applicant: Yahoo! Inc.

Inventor： Kunal Punera ,  Deepayan Chakrabarti

IPC: G06F15/18 ,  G06N5/00 ,  G06F17/30 ,  G06N7/00

CPC classification number: G06F17/30864 ,  G06N7/005

Abstract: A method and apparatus for receiving training data that comprise a plurality of event-and-time-specific texts that are contextually related to a plurality of events; iteratively processing the training data to generate a modified network model that defines a plurality of states; receiving additional data that comprise a plurality of additional event-and-time-specific texts that are contextually related to a particular event; processing the additional data by applying the modified network model to the additional data to identify, within the plurality of additional event-and-time specific texts, a particular set of texts that belong to a particular state of the plurality of states; identifying, within the particular set of texts, one or more texts that are most representative of all texts in the particular set of texts that belong to the particular state; wherein the method is performed by one or more special-purpose computing devices.

Abstract translation: 一种用于接收训练数据的方法和装置，所述培训数据包括与多个事件相关的多个事件和时间专用文本; 迭代地处理训练数据以生成定义多个状态的修改的网络模型; 接收包括与特定事件上下文相关的多个附加事件和时间特定文本的附加数据; 通过将修改的网络模型应用于附加数据来处理附加数据，以在多个附加事件和时间特定文本内识别属于多个状态的特定状态的特定文本集合; 在特定文本集中确定一个或多个文本，其最具代表属于特定国家的特定文本集中的所有文本; 其中所述方法由一个或多个专用计算设备执行。

公开(公告)号：US20140108327A1

公开(公告)日：2014-04-17

申请号：US14136477

申请日：2013-12-20

Applicant: YAHOO! INC.

Inventor： Theodoros Lappas ,  Kunal Punera ,  Tamas Sarlos

IPC: G06N7/00

CPC classification number: G06F17/30268 ,  G06F17/30876 ,  G06F17/30958 ,  G06N7/005 ,  G06Q10/00

Abstract: Descriptive data relating to at least a subset of a plurality of entities on a website is retrieved over a network. Endorsement data relating to the plurality of entities is retrieved from the website. A first set of probabilities is determined reflecting a probability that endorsements can be attributed to specific aspects. A second set of probabilities is determined reflecting a probability that terms can be attributed to aspects. Using the first set of probabilities and the second set of probabilities, a subset of the terms that are most probably associated with each entity are selected. Tags are then generated for each entity using the selected terms.

Abstract translation: 通过网络检索与网站上的多个实体的至少一个子集有关的描述性数据。 从网站检索与多个实体相关的认可数据。 确定第一组概率，反映出可以将认可归因于特定方面的概率。 确定第二组概率，反映术语可归因于方面的概率。 使用第一组概率和第二组概率，选择最可能与每个实体相关联的项的子集。 然后使用所选项来为每个实体生成标签。

公开(公告)号：US20160171082A1

公开(公告)日：2016-06-16

申请号：US15052725

申请日：2016-02-24

Applicant: Yahoo! Inc.

Inventor： Kunal Punera ,  Deepayan Chakrabarti ,  Xuanhui Wang

IPC: G06F17/30

CPC classification number: G06F16/285 ,  G06F16/2425 ,  G06F16/2465 ,  G06F16/9535

Abstract: An optimization-based framework is utilized to extract broad query aspects from query reformulations performed by users in historical user session logs. Objective functions are optimized to yield query aspects. At run-time, the best broad but unspecified query aspects relevant to any user query are presented along with the results of the run time query.

Abstract translation: 利用基于优化的框架从历史用户会话日志中的用户执行的查询重新设计中提取广泛的查询方面。 优化目标函数以产生查询方面。 在运行时，与任何用户查询相关的最佳广泛但未指定的查询方面与运行时查询的结果一起显示。