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公开(公告)号：US20160162456A1
公开(公告)日：2016-06-09
申请号：US14964517
申请日：2015-12-09
申请人： Robert J. Munro , Schuyler D. Erle , Christopher Walker , Sarah K. Luger , Jason Brenier , Gary C. King , Paul A. Tepper , Ross Mechanic , Andrew Gilchrist-Scott , Jessica D. Long , James B. Robinson , Brendan D. Callahan , Michelle Casbon , Ujjwal Sarin , Aneesh Nair , Veena Basavaraj , Tripti Saxena , Edgar Nunez , Martha G. Hinrichs , Haley Most , Tyler J. Schnoebelen
发明人： Robert J. Munro , Schuyler D. Erle , Christopher Walker , Sarah K. Luger , Jason Brenier , Gary C. King , Paul A. Tepper , Ross Mechanic , Andrew Gilchrist-Scott , Jessica D. Long , James B. Robinson , Brendan D. Callahan , Michelle Casbon , Ujjwal Sarin , Aneesh Nair , Veena Basavaraj , Tripti Saxena , Edgar Nunez , Martha G. Hinrichs , Haley Most , Tyler J. Schnoebelen
CPC分类号： G06F17/30598 , G06F3/0482 , G06F17/2241 , G06F17/241 , G06F17/272 , G06F17/2785 , G06F17/28 , G06F17/2809 , G06F17/30011 , G06F17/30401 , G06F17/30445 , G06F17/30604 , G06F17/30654 , G06F17/30705 , G06F17/30734 , G06F17/30864 , G06Q50/01
摘要： Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.
摘要翻译： 提出了生成自然语言模型的方法。 该方法可以包括：摄取表示要由自然语言模型分析的文档的训练数据，生成包括至少两个主题节点的分层数据结构，训练数据将在该节点内被自然语言模型细分，选择多个 在要注释的训练数据中生成文档的注释提示，为每个文档生成关于所述文档的注释的注释提示，该注释指示所述文档中的至少两个主题节点中的哪个节点被分类，基于注释接收注释 提示; 以及使用自适应机器学习过程来生成所述自然语言模型，所述自适应机器学习过程被配置为根据所述分级数据结构的所述至少两个主题节点来确定所述注释中的模式如何根据所述训练数据中的文档被细分。
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公开(公告)号：US20160162569A1
公开(公告)日：2016-06-09
申请号：US14964510
申请日：2015-12-09
申请人： Schuyler D. Erle , Robert J. Munro , Brendan D. Callahan , Jason Brenier , Paul A. Tepper , Jessica D. Long , James B. Robinson , Aneesh Nair , Michelle Casbon , Stefan Krawczyk
发明人： Schuyler D. Erle , Robert J. Munro , Brendan D. Callahan , Jason Brenier , Paul A. Tepper , Jessica D. Long , James B. Robinson , Aneesh Nair , Michelle Casbon , Stefan Krawczyk
CPC分类号： G06F17/241 , G06F3/0482 , G06F16/243 , G06F16/24532 , G06F16/285 , G06F16/288 , G06F16/3329 , G06F16/35 , G06F16/367 , G06F16/93 , G06F16/951 , G06F17/2241 , G06F17/272 , G06F17/2785 , G06F17/28 , G06F17/2809 , G06Q50/01
摘要： Systems and methods are presented for providing improved machine performance in natural language processing. In some example embodiments, an API module is presented that is configured to drive processing of a system architecture for natural language processing. Aspects of the present disclosure allow for a natural language model to classify documents while other documents are being retrieved in real time. The natural language model and the documents are configured to be stored in a stateless format, which also allows for additional functions to be performed on the documents while the natural language model is used to continue classifying other documents.
摘要翻译： 提出了系统和方法，用于在自然语言处理中提供改进的机器性能。 在一些示例性实施例中，呈现了被配置为驱动用于自然语言处理的系统架构的处理的API模块。 本公开的方面允许自然语言模型对文档进行分类，而实时检索其他文档。 自然语言模型和文档被配置为以无状态格式存储，这也允许在文档上执行附加功能，同时使用自然语言模型继续对其他文档进行分类。
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公开(公告)号：US20190243886A1
公开(公告)日：2019-08-08
申请号：US16125343
申请日：2018-09-07
申请人： Schuyler D. Erle , Robert J. Munro , Brendan D. Callahan , Jason Brenier , Paul A. Tepper , Jessica D. Long , James B. Robinson , Aneesh Nair , Michelle Casbon , Stefan Krawczyk
发明人： Schuyler D. Erle , Robert J. Munro , Brendan D. Callahan , Jason Brenier , Paul A. Tepper , Jessica D. Long , James B. Robinson , Aneesh Nair , Michelle Casbon , Stefan Krawczyk
IPC分类号： G06F17/24 , G06F16/332 , G06F17/28 , G06F16/28 , G06F16/93 , G06F16/35 , G06F16/2453 , G06F16/951 , G06F16/242 , G06F17/22 , G06Q50/00 , G06F17/27 , G06F3/0482 , G06F16/36
CPC分类号： G06F17/241 , G06F3/0482 , G06F16/243 , G06F16/24532 , G06F16/285 , G06F16/288 , G06F16/3329 , G06F16/35 , G06F16/367 , G06F16/93 , G06F16/951 , G06F17/2241 , G06F17/272 , G06F17/2785 , G06F17/28 , G06F17/2809 , G06Q50/01
摘要： Systems and methods are presented for providing improved machine performance in natural language processing. In some example embodiments, an API module is presented that is configured to drive processing of a system architecture for natural language processing. Aspects of the present disclosure allow for a natural language model to classify documents while other documents are being retrieved in real time. The natural language model and the documents are configured to be stored in a stateless format, which also allows for additional functions to be performed on the documents while the natural language model is used to continue classifying other documents.
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