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    • 2. 发明授权
    • Reclassification of training data to improve classifier accuracy
    • 重新分类训练数据,提高分类精度
    • US09342588B2
    • 2016-05-17
    • US11764291
    • 2007-06-18
    • Rajesh BalchandranLinda M. BoyerGregory Purdy
    • Rajesh BalchandranLinda M. BoyerGregory Purdy
    • G06F17/27G06F17/30
    • G06F17/30705
    • A method of creating a statistical classification model for a classifier within a natural language understanding system can include processing training data using an existing statistical classification model. Sentences of the training data correctly classified into a selected class of the statistical classification model can be selected. The selected sentences of the training data can be assigned to a fringe group or a core group according to confidence score. The training data can be updated by associating the fringe group with a fringe subclass of the selected class and the core group with a core subclass of the selected class. A new statistical classification model can be built from the updated training data. The new statistical classification model can be output.
    • 在自然语言理解系统内创建用于分类器的统计分类模型的方法可以包括使用现有的统计分类模型处理训练数据。 可以选择正确分类为所选类别的统计分类模型的训练数据句子。 训练数据的所选句子可以根据置信度得分分配给边缘组或核心组。 可以通过将边缘组与所选类的边缘子类和具有所选类的核心子类的核心组相关联来更新训练数据。 可以从更新的训练数据构建新的统计分类模型。 可以输出新的统计分类模型。
    • 3. 发明授权
    • Sub-model generation to improve classification accuracy
    • 子模型生成提高分类精度
    • US09058319B2
    • 2015-06-16
    • US11764274
    • 2007-06-18
    • Rajesh BalchandranLinda M. BoyerGregory Purdy
    • Rajesh BalchandranLinda M. BoyerGregory Purdy
    • G06F17/27
    • G06F17/2715
    • A method of classifying text input for use with a natural language understanding system can include determining classification information including a primary classification and one or more secondary classifications for a received text input using a statistical classification model (statistical model). A statistical classification sub-model (statistical sub-model) can be selectively built according to a model generation criterion applied to the classification information. The method further can include selecting the primary classification or the secondary classification for the text input as a final classification according to the statistical sub-model and outputting the final classification for the text input.
    • 用于分类文本输入以与自然语言理解系统一起使用的方法可以包括使用统计分类模型(统计模型)确定包括主分类的分类信息和用于所接收的文本输入的一个或多个次分类。 可以根据应用于分类信息的模型生成准则选择性地建立统计分类子模型(统计子模型)。 该方法还可以包括根据统计子模型选择文本输入的主分类或次级分类作为最终分类,并输出文本输入的最终分类。
    • 4. 发明授权
    • Method and system for conveying an example in a natural language understanding application
    • 用于传达自然语言理解应用中的示例的方法和系统
    • US08612229B2
    • 2013-12-17
    • US11300799
    • 2005-12-15
    • Rajesh BalchandranLinda M. BoyerJames R. LewisBrent D. Metz
    • Rajesh BalchandranLinda M. BoyerJames R. LewisBrent D. Metz
    • G10L15/00G10L15/18G10L21/00G10L25/00
    • G10L15/063G06F17/28G10L15/18G10L15/183G10L2015/0638H04M3/4936H04M2203/2061
    • A method (300) and system (100) is provided to add the creation of examples at a developer level in the generation of Natural Language Understanding (NLU) models, tying the examples into a NLU sentence database (130), automatically validating (310) a correct outcome of using the examples, and automatically resolving (316) problems the user has using the examples. The method (300) can convey examples of what a caller can say to a Natural Language Understanding (NLU) application. The method includes entering at least one example associated with an existing routing destination, and ensuring an NLU model correctly interprets the example unambiguously for correctly routing a call to the routing destination. The method can include presenting the example sentence in a help message (126) within an NLU dialogue as an example of what a caller can say for connecting the caller to a desired routing destination. The method can also include presented a failure dialogue for displaying at least one example that failed to be properly interpreted to ensure that ambiguous or incorrect examples are not presented in a help message.
    • 提供了一种方法(300)和系统(100),用于在生成自然语言理解(NLU)模型时在开发者级别添加示例的创建,将示例绑定到NLU句子数据库(130)中,自动验证(310 )使用示例的正确结果,并自动解析(316)用户使用示例的问题。 方法(300)可以传达呼叫者可以对自然语言理解(NLU)应用程序说什么的示例。 该方法包括输入与现有路由目的地相关联的至少一个示例,并且确保NLU模型明确地解释该示例以正确路由呼叫到路由目的地。 该方法可以包括将NLU对话内的帮助消息(126)中的示例呈现为呼叫者可以将呼叫者连接到期望的路由目的地的示例。 该方法还可以包括呈现失败对话,用于显示未被正确解释的至少一个示例,以确保在帮助消息中不呈现模糊或不正确的示例。