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    System and a method for generating semantically similar sentences for building a robust SLM
    42.
    发明授权
    System and a method for generating semantically similar sentences for building a robust SLM 有权
    系统和一种用于生成语义上相似的句子来构建稳健的SLM的方法

    公开(公告)号：US09135237B2

    公开(公告)日：2015-09-15

    申请号：US13181923

    申请日：2011-07-13

    Applicant: Om D. Deshmukh ,  Sachindra Joshi ,  Shajith I. Mohamed ,  Ashish Verma

    Inventor： Om D. Deshmukh ,  Sachindra Joshi ,  Shajith I. Mohamed ,  Ashish Verma

    IPC: G06F17/27 ,  G10L15/26 ,  G06F17/28

    CPC classification number: G06F17/274 ,  G06F17/2795 ,  G06F17/2881 ,  G10L15/26

    Abstract: A system and method are described for generating semantically similar sentences for a statistical language model. A semantic class generator determines for each word in an input utterance a set of corresponding semantically similar words. A sentence generator computes a set of candidate sentences each containing at most one member from each set of semantically similar words. A sentence verifier grammatically tests each candidate sentence to determine a set of grammatically correct sentences semantically similar to the input utterance. Also note that the generated semantically similar sentences are not restricted to be selected from an existing sentence database.

    Abstract translation: 描述了用于为统计语言模型生成语义上类似的句子的系统和方法。 语义类生成器确定输入语义中的每个单词一组相应的语义上相似的单词。 句子生成器从每个语义上相似的单词集合中计算出一组候选句子，每个候选句子最多包含一个成员。 句子验证器语法测试每个候选句子以确定一组语法上正确的句子，其语义上类似于输入的话语。 还要注意，生成的语义上相似的句子不限于从现有句子数据库中选择。

    Method and apparatus for populating a predefined concept hierarchy or other hierarchical set of classified data items by minimizing system entrophy
    48.
    发明授权
    Method and apparatus for populating a predefined concept hierarchy or other hierarchical set of classified data items by minimizing system entrophy 失效
    用于通过最小化系统萎缩来填充预定义概念层级或其他分层数据集合的方法和装置

    公开(公告)号：US07320000B2

    公开(公告)日：2008-01-15

    申请号：US10309612

    申请日：2002-12-04

    Applicant: Krishna Prasad Chitrapura ,  Raghuram Krishnapuram ,  Sachindra Joshi

    Inventor： Krishna Prasad Chitrapura ,  Raghuram Krishnapuram ,  Sachindra Joshi

    IPC: G06F7/10

    CPC classification number: G06F17/30 ,  Y10S707/99937

    Abstract: A system and method for automated populating of an existing concept hierarchy of items with new items, using entropy as a measure of the correctness of a potential classification. User-defined concept hierarchies include, for example, document hierarchies such as directories for the Internet, library catalogues, patent databases and journals, and product hierarchies. These concept hierarchies can be huge and are usually maintained manually. An internet directory may have, for example, millions of Web sites, thousands of editors and hundreds of thousands of different categories. The method for populating a concept hierarchy includes calculating conditional ‘entropy’ values representing the randomness of distribution of classification attributes for the hierarchical set of classes if a new item is added to specific classes of the hierarchy and then selecting whichever class has the minimum randomness of distribution when calculated as a condition of insertion of the new data item.

    Abstract translation: 一种使用熵作为潜在分类正确性的量度来自动填充具有新项目的项目的现有概念层次结构的系统和方法。 用户定义的概念层次结构包括例如文档层次结构，例如因特网的目录，图书馆目录，专利数据库和期刊以及产品层次结构。 这些概念层次结构可以是巨大的，通常是手动维护的。 互联网目录可能具有数百万个网站，数千个编辑者和数十万个不同类别。 用于填充概念层次的方法包括：如果将新项目添加到层级的特定类别，然后选择哪个类别具有最小随机性，则计算表示分级集合类的分类属性的分布随机性的条件“熵值” 当作为插入新数据项的条件计算时的分配。

    Methods, apparatus and computer programs for evaluating and using a resilient data representation
    49.
    发明授权
    Methods, apparatus and computer programs for evaluating and using a resilient data representation 有权
    用于评估和使用弹性数据表示的方法，装置和计算机程序

    公开(公告)号：US07254577B2

    公开(公告)日：2007-08-07

    申请号：US10880141

    申请日：2004-06-29

    Applicant: Rahul Gupta ,  Sachindra Joshi ,  Raghuram Krishnapuram

    Inventor： Rahul Gupta ,  Sachindra Joshi ,  Raghuram Krishnapuram

    IPC: G06F17/30

    CPC classification number: G06F17/30914 ,  Y10S707/99932 ,  Y10S707/99936 ,  Y10S707/99942

    Abstract: Provided are methods, apparatus and computer programs for evaluating the resilience, to structural changes in a data source, of a representative label representing a data element within the data source. Also disclosed are applications using a resilient representative label. For example, a representative label may represent a particular data field or other data element within a semi-structured data source—such as within XML or HTML Web pages. An estimate of resilience to changes can be used to determine whether a candidate representative label satisfies a required degree of resilience, or to enable selection of a label with the highest resilience score among a set of representative labels. The validated or selected representative label may then be used for data extraction, remaining usable despite the possibility of future changes to the structure of a Web page, or for template clustering/classification.

    Abstract translation: 提供了用于评估表示数据源中的数据元素的代表性标签的弹性（数据源中的结构变化）的方法，装置和计算机程序。 还公开了使用弹性代表性标签的应用。 例如，代表性标签可以表示半结构化数据源中的特定数据字段或其他数据元素，例如在XML或HTML网页内。 可以使用对变化的弹性的估计来确定候选代表标签是否满足所需的弹性程度，或者使得能够在一组代表性标签中选择具有最高回弹分数的标签。 经验证或选择的代表性标签然后可用于数据提取，尽管可能将来会改变网页的结构，或用于模板聚类/分类，仍然可用。

    System and method for extraction of factoids from textual repositories
    50.
    发明申请
    System and method for extraction of factoids from textual repositories 失效
    从文本库中提取事实的系统和方法

    公开(公告)号：US20070162447A1

    公开(公告)日：2007-07-12

    申请号：US11321177

    申请日：2005-12-29

    Applicant: Sachindra Joshi ,  Raghuram Krishnapuram ,  Nimit Kumar ,  Kiran Mehta ,  Sumit Negi ,  Ganesh Ramakrishnan ,  Scott Holmes

    Inventor： Sachindra Joshi ,  Raghuram Krishnapuram ,  Nimit Kumar ,  Kiran Mehta ,  Sumit Negi ,  Ganesh Ramakrishnan ,  Scott Holmes

    IPC: G06F7/00

    CPC classification number: G06F17/30864 ,  G06F17/30705

    Abstract: A method (400) is disclosed of extracting factoids from text repositories, with the factoids being associated with a given factoid category. The method (400) starts by training a classifier (230) to recognise factoids relevant to that given factoid category. Documents or document summaries relevant to the given factoid category is next collected (410) from the text repositories. Sentences having a predetermined association to the given factoid category is extracted (420) from the documents or said document summaries. Those sentences are classified (440), in a noisy environment, using the classifier (230) to extract snippets containing phrases relevant to the given factoid category. It is the extracted snippets that are the factoid associated with the given factoid category.

    Abstract translation: 公开了一种从文本存储库中提取事实框架的方法（400），其中事实框架与给定的类别类别相关联。 方法（400）通过训练分类器（230）开始，以识别与该给定的类别类别相关的因子。 接下来从文本存储库收集与文件类型相关的文档或文档摘要（410）。 具有与给定类别类别的预定关联的句子从文档或所述文档摘要中提取（420）。 这些句子在嘈杂的环境中被分类（440），使用分类器（230）提取包含与给定类别类别相关的短语的片段。 提取的片段是与给定类实体类别相关联的实例。

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