RANKER SELECTION FOR STATISTICAL NATURAL LANGUAGE PROCESSING
    1.
    发明申请
    RANKER SELECTION FOR STATISTICAL NATURAL LANGUAGE PROCESSING 有权
    用于统计自然语言处理的排名选择

    公开(公告)号:US20090125501A1

    公开(公告)日:2009-05-14

    申请号:US11938811

    申请日:2007-11-13

    IPC分类号: G06F7/10

    CPC分类号: G06F17/2715

    摘要: Systems and methods for selecting a ranker for statistical natural language processing are provided. One disclosed system includes a computer program configured to be executed on a computing device, the computer program comprising a data store including reference performance data for a plurality of candidate rankers, the reference performance data being calculated based on a processing of test data by each of the plurality of candidate rankers. The system may further include a ranker selector configured to receive a statistical natural language processing task and a performance target, and determine a selected ranker from the plurality of candidate rankers based on the statistical natural language processing task, the performance target, and the reference performance data.

    摘要翻译: 提供了用于选择用于统计自然语言处理的游戏者的系统和方法。 一种公开的系统包括被配置为在计算设备上执行的计算机程序,该计算机程序包括数据存储器,该数据存储器包括用于多个候选排名者的参考演出数据,该参考演出数据是基于每个测试数据的处理来计算的 多个候选排名。 该系统可以进一步包括配置成接收统计自然语言处理任务和性能目标的排队选择器,并且基于统计自然语言处理任务,性能目标和参考性能来确定来自多个候选排名者的选定队员 数据。

    Ranker selection for statistical natural language processing
    2.
    发明授权
    Ranker selection for statistical natural language processing 有权
    统计自然语言处理的Ranker选择

    公开(公告)号:US07844555B2

    公开(公告)日:2010-11-30

    申请号:US11938811

    申请日:2007-11-13

    CPC分类号: G06F17/2715

    摘要: Systems and methods for selecting a ranker for statistical natural language processing are provided. One disclosed system includes a computer program configured to be executed on a computing device, the computer program comprising a data store including reference performance data for a plurality of candidate rankers, the reference performance data being calculated based on a processing of test data by each of the plurality of candidate rankers. The system may further include a ranker selector configured to receive a statistical natural language processing task and a performance target, and determine a selected ranker from the plurality of candidate rankers based on the statistical natural language processing task, the performance target, and the reference performance data.

    摘要翻译: 提供了用于选择用于统计自然语言处理的游戏者的系统和方法。 一种公开的系统包括被配置为在计算设备上执行的计算机程序,该计算机程序包括数据存储器,该数据存储器包括用于多个候选排名者的参考演出数据,该参考演出数据是基于每个测试数据的处理来计算的 多个候选排名。 该系统可以进一步包括配置成接收统计自然语言处理任务和性能目标的排队选择器,并且基于统计自然语言处理任务,性能目标和参考性能来确定来自多个候选排名者的选定队员 数据。

    Weighted linear model
    3.
    发明申请
    Weighted linear model 审中-公开
    加权线性模型

    公开(公告)号:US20070083357A1

    公开(公告)日:2007-04-12

    申请号:US11485015

    申请日:2006-07-12

    IPC分类号: G06F17/28

    CPC分类号: G06F17/2827 G06F17/2836

    摘要: A weighted linear word alignment model linearly combines weighted features to score a word alignment for a bilingual, aligned pair of text fragments. The features are each weighted by a feature weight. One of the features is a word association metric, which may be generated from surface statistics.

    摘要翻译: 加权线性字对齐模型线性组合加权特征以对双语对齐的文本片段对进行字对齐。 特征各自由特征权重加权。 特征之一是字关联度量,其可以从表面统计量生成。

    Limited-memory quasi-newton optimization algorithm for L1-regularized objectives
    4.
    发明授权
    Limited-memory quasi-newton optimization algorithm for L1-regularized objectives 有权
    L1规范化目标的有限存储准牛顿优化算法

    公开(公告)号:US07933847B2

    公开(公告)日:2011-04-26

    申请号:US11874199

    申请日:2007-10-17

    CPC分类号: G06N99/005

    摘要: An algorithm that employs modified methods developed for optimizing differential functions but which can also handle the special non-differentiabilities that occur with the L1-regularization. The algorithm is a modification of the L-BFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno) quasi-Newton algorithm, but which can now handle the discontinuity of the gradient using a procedure that chooses a search direction at each iteration and modifies the line search procedure. The algorithm includes an iterative optimization procedure where each iteration approximately minimizes the objective over a constrained region of the space on which the objective is differentiable (in the case of L1-regularization, a given orthant), models the second-order behavior of the objective by considering the loss component alone, using a “line-search” at each iteration that projects search points back onto the chosen orthant, and determines when to stop the line search.

    摘要翻译: 一种使用为优化差分功能而开发的修改方法的算法,但也可以处理L1正则化发生的特殊非差异性。 该算法是L-BFGS(有限存储器Broyden-Fletcher-Goldfarb-Shanno)准牛顿算法的修改,但现在可以使用在每次迭代中选择搜索方向的过程来处理梯度的不连续性,并且修改 线搜索程序。 该算法包括一个迭代优化过程,其中每次迭代大致使目标在目标可微分的空间的约束区域(在L1正则化的情况下,给定的不对称)下的目标最小化,对目标的二阶行为进行建模 通过考虑单独的损失组件,在每次迭代时使用“线搜索”来将搜​​索点投射回所选择的不同,并确定何时停止线搜索。

    LIMITED-MEMORY QUASI-NEWTON OPTIMIZATION ALGORITHM FOR L1-REGULARIZED OBJECTIVES
    5.
    发明申请
    LIMITED-MEMORY QUASI-NEWTON OPTIMIZATION ALGORITHM FOR L1-REGULARIZED OBJECTIVES 有权
    用于L1规范化目标的有限存储器QUASI-NEWTON优化算法

    公开(公告)号:US20090106173A1

    公开(公告)日:2009-04-23

    申请号:US11874199

    申请日:2007-10-17

    IPC分类号: G06F15/18

    CPC分类号: G06N99/005

    摘要: An algorithm that employs modified methods developed for optimizing differential functions but which can also handle the special non-differentiabilities that occur with the L1-regularization. The algorithm is a modification of the L-BFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno) quasi-Newton algorithm, but which can now handle the discontinuity of the gradient using a procedure that chooses a search direction at each iteration and modifies the line search procedure. The algorithm includes an iterative optimization procedure where each iteration approximately minimizes the objective over a constrained region of the space on which the objective is differentiable (in the case of L1-regularization, a given orthant), models the second-order behavior of the objective by considering the loss component alone, using a “line-search” at each iteration that projects search points back onto the chosen orthant, and determines when to stop the line search.

    摘要翻译: 一种使用为优化差分功能而开发的修改方法的算法,但也可以处理L1正则化发生的特殊非差异性。 该算法是L-BFGS(有限存储器Broyden-Fletcher-Goldfarb-Shanno)准牛顿算法的修改,但现在可以使用在每次迭代中选择搜索方向的过程来处理梯度的不连续性,并且修改 线搜索程序。 该算法包括一个迭代优化过程,其中每次迭代大致使目标在目标可微分的空间的约束区域(在L1正则化的情况下,给定的不对称)下的目标最小化,对目标的二阶行为进行建模 通过考虑单独的损失组件,在每次迭代时使用“线搜索”来将搜​​索点投射回所选择的不同,并确定何时停止线搜索。

    DETERMINING A SIMILARITY MEASURE BETWEEN QUERIES
    7.
    发明申请
    DETERMINING A SIMILARITY MEASURE BETWEEN QUERIES 有权
    确定查询之间的相似度

    公开(公告)号:US20100325133A1

    公开(公告)日:2010-12-23

    申请号:US12488603

    申请日:2009-06-22

    IPC分类号: G06F17/30

    CPC分类号: G06F17/30864

    摘要: A system described herein includes a receiver component that receives a dataset that is stored in a computer-readable medium of a computing device, wherein the dataset includes a plurality of queries issued by users to a search engine and a plurality of search results selected by the users upon issuing the plurality of queries. A distribution determiner component determines click distributions over the search results selected by the users with respect to the plurality of queries. A labeler component labels at least two queries in the plurality of queries as being substantially similar to one another based at least in part upon the click distributions over the search results selected by the users with respect to the plurality of queries.

    摘要翻译: 本文描述的系统包括接收存储在计算设备的计算机可读介质中的数据集的接收器组件,其中所述数据集包括用户向搜索引擎发出的多个查询以及由所述搜索引擎选择的多个搜索结果 用户在发出多个查询时。 分布确定器组件确定用户相对于多个查询选择的搜索结果的点击分布。 标签器组件至少部分地基于用户相对于多个查询选择的搜索结果上的点击分布,将多个查询中的至少两个查询标记为彼此基本相似。

    Determining a similarity measure between queries
    9.
    发明授权
    Determining a similarity measure between queries 有权
    确定查询之间的相似性度量

    公开(公告)号:US08606786B2

    公开(公告)日:2013-12-10

    申请号:US12488603

    申请日:2009-06-22

    IPC分类号: G06F7/00 G06F17/30

    CPC分类号: G06F17/30864

    摘要: A system described herein includes a receiver component that receives a dataset that is stored in a computer-readable medium of a computing device, wherein the dataset includes a plurality of queries issued by users to a search engine and a plurality of search results selected by the users upon issuing the plurality of queries. A distribution determiner component determines click distributions over the search results selected by the users with respect to the plurality of queries. A labeler component labels at least two queries in the plurality of queries as being substantially similar to one another based at least in part upon the click distributions over the search results selected by the users with respect to the plurality of queries.

    摘要翻译: 本文描述的系统包括接收存储在计算设备的计算机可读介质中的数据集的接收器组件,其中所述数据集包括用户向搜索引擎发出的多个查询以及由所述搜索引擎选择的多个搜索结果 用户在发出多个查询时。 分布确定器组件确定用户相对于多个查询选择的搜索结果的点击分布。 标签器组件至少部分地基于用户相对于多个查询选择的搜索结果上的点击分布,将多个查询中的至少两个查询标记为彼此基本相似。