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    Search lexicon expansion
    1.
    发明授权
    Search lexicon expansion 有权

    公开(公告)号：US09928296B2

    公开(公告)日：2018-03-27

    申请号：US12970477

    申请日：2010-12-16

    申请人： Xiao Li ,  Jingjing Liu ,  Alejandro Acero ,  Ye-Yi Wang

    发明人： Xiao Li ,  Jingjing Liu ,  Alejandro Acero ,  Ye-Yi Wang

    IPC分类号： G06F17/30 ,  G06F17/27

    CPC分类号： G06F17/30737 ,  G06F17/2735 ,  G06F17/30693 ,  G06F17/30864

    摘要： One or more techniques and/or systems are disclosed for creating an expanded or improved lexicon for use in search-based semantic tagging. A set of first documents can be identified using a set of first lexicon elements as queries, and one or more first document patterns can be extracted from the set of first documents. The document patterns can be used to find one or more second documents in a query log that comprise the document patterns, which are associated with query terms used to return the second documents. The query terms for the second documents can be extracted and used to expand the lexicon. Elements within the lexicon may be weighted based upon relevance to different query domains, for example.

    SEARCH LEXICON EXPANSION
    2.
    发明申请
    SEARCH LEXICON EXPANSION 有权
    搜索LEXICON EXPANSION

    公开(公告)号：US20120158703A1

    公开(公告)日：2012-06-21

    申请号：US12970477

    申请日：2010-12-16

    申请人： Xiao Li ,  Jingjing Liu ,  Alejandro Acero ,  Ye-Yi Wang

    发明人： Xiao Li ,  Jingjing Liu ,  Alejandro Acero ,  Ye-Yi Wang

    IPC分类号： G06F17/30

    CPC分类号： G06F17/30737 ,  G06F17/2735 ,  G06F17/30693 ,  G06F17/30864

    摘要： One or more techniques and/or systems are disclosed for creating an expanded or improved lexicon for use in search-based semantic tagging. A set of first documents can be identified using a set of first lexicon elements as queries, and one or more first document patterns can be extracted from the set of first documents. The document patterns can be used to find one or more second documents in a query log that comprise the document patterns, which are associated with query terms used to return the second documents. The query terms for the second documents can be extracted and used to expand the lexicon. Elements within the lexicon may be weighted based upon relevance to different query domains, for example.

    摘要翻译： 公开了一种或多种技术和/或系统，用于创建用于基于搜索的语义标签中的扩展或改进的词典。 可以使用一组第一词典元素作为查询来识别一组第一文档，并且可以从该组第一文档中提取一个或多个第一文档图案。 文档模式可用于在查询日志中找到构成文档模式的一个或多个第二文档，这些文档模式与用于返回第二个文档的查询术语相关联。 可以提取和使用第二个文档的查询条款来扩展词典。 例如，词法中的元素可以基于与不同查询域的相关性来加权。

    Indexing and ranking processes for directory assistance services
    5.
    发明授权
    Indexing and ranking processes for directory assistance services 有权
    目录援助服务的索引和排名流程

    公开(公告)号：US07580942B2

    公开(公告)日：2009-08-25

    申请号：US11652733

    申请日：2007-01-12

    申请人： Dong Yu ,  Alejandro Acero ,  Yun-Cheng Ju ,  Ye-Yi Wang

    发明人： Dong Yu ,  Alejandro Acero ,  Yun-Cheng Ju ,  Ye-Yi Wang

    IPC分类号： G06F17/30

    CPC分类号： G06F17/30663 ,  Y10S707/99933 ,  Y10S707/99935 ,  Y10S707/99942

    摘要： A computer-implemented method is disclosed for providing a directory assistance service. The method includes generating an indexing file that is a representation of information associated with a collection of listings stored in an index. The indexing file is utilized as a basis for ranking listings in an index based on the strength of association with a query. Based at least in part on the ranking, an output is provided and is indicative of listings in the index that are likely correspond to the query. At least one particular listing in the index is excluded from the output without there ever being a comparison of features in the query with features in the one particular listing.

    摘要翻译： 公开了一种用于提供目录辅助服务的计算机实现的方法。 该方法包括生成索引文件，其是与存储在索引中的列表的集合相关联的信息的表示。 基于与查询的关联强度，索引文件被用作在索引中对列表进行排名的基础。 至少部分地基于排名，提供输出并且指示索引中可能对应于查询的列表。 索引中的至少一个特定列表从输出中排除，而不会将查询中的功能与特定列表中的功能进行比较。

    MAXIMUM ENTROPY MODEL PARAMETERIZATION
    6.
    发明申请
    MAXIMUM ENTROPY MODEL PARAMETERIZATION 有权
    最大熵模型参数

    公开(公告)号：US20090150308A1

    公开(公告)日：2009-06-11

    申请号：US11952130

    申请日：2007-12-07

    申请人： Ye-Yi Wang ,  Alejandro Acero

    发明人： Ye-Yi Wang ,  Alejandro Acero

    IPC分类号： G06F15/18

    CPC分类号： G06K9/723 ,  G06K9/00483 ,  G06K9/6278

    摘要： Described is a technology by which a maximum entropy model used for classification is trained with a significantly lesser amount of training data than is normally used in training other maximum entropy models, yet provides similar accuracy to the others. The maximum entropy model is initially parameterized with parameter values determined from weights obtained by training a vector space model or an n-gram model. The weights may be scaled into the initial parameter values by determining a scaling factor. Gaussian mean values may also be determined, and used for regularization in training the maximum entropy model. Scaling may also be applied to the Gaussian mean values. After initial parameterization, training comprises using training data to iteratively adjust the initial parameters into adjusted parameters until convergence is determined.

    摘要翻译： 描述了一种技术，通过该技术，用于分类的最大熵模型以比通常在训练其他最大熵模型中通常使用的训练数据少得多的训练来训练，但是提供了与其他最大熵模型相似的精度。 最大熵模型最初参数化，其参数值由通过训练向量空间模型或n-gram模型获得的权重确定。 通过确定缩放因子，权重可以被缩放到初始参数值中。 也可以确定高斯平均值，并用于训练最大熵模型的正则化。 缩放也可以应用于高斯平均值。 在初始参数化之后，训练包括使用训练数据将初始参数迭代地调整为调整参数，直到确定收敛。

    Maximum entropy model classfier that uses gaussian mean values
    10.
    发明授权
    Maximum entropy model classfier that uses gaussian mean values 有权
    最大熵模型类使用高斯平均值

    公开(公告)号：US07925602B2

    公开(公告)日：2011-04-12

    申请号：US11952130

    申请日：2007-12-07

    申请人： Ye-Yi Wang ,  Alejandro Acero

    发明人： Ye-Yi Wang ,  Alejandro Acero

    IPC分类号： G06E1/00

    CPC分类号： G06K9/723 ,  G06K9/00483 ,  G06K9/6278

    摘要： Described is a technology by which a maximum entropy model used for classification is trained with a significantly lesser amount of training data than is normally used in training other maximum entropy models, yet provides similar accuracy to the others. The maximum entropy model is initially parameterized with parameter values determined from weights obtained by training a vector space model or an n-gram model. The weights may be scaled into the initial parameter values by determining a scaling factor. Gaussian mean values may also be determined, and used for regularization in training the maximum entropy model. Scaling may also be applied to the Gaussian mean values. After initial parameterization, training comprises using training data to iteratively adjust the initial parameters into adjusted parameters until convergence is determined.

    摘要翻译： 描述了一种技术，通过该技术，用于分类的最大熵模型以比通常在训练其他最大熵模型中通常使用的训练数据少得多的训练来训练，但是提供了与其他最大熵模型相似的精度。 最大熵模型最初参数化，其参数值由通过训练向量空间模型或n-gram模型获得的权重确定。 通过确定缩放因子，权重可以被缩放到初始参数值中。 也可以确定高斯平均值，并用于训练最大熵模型的正则化。 缩放也可以应用于高斯平均值。 在初始参数化之后，训练包括使用训练数据将初始参数迭代地调整为调整参数，直到确定收敛。