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1.发明申请公开(公告)号：US20100235165A1
公开(公告)日：2010-09-16
申请号：US12723472
申请日：2010-03-12
申请人： James Todhunter , Igor Sovpel , Dzianis Pastanohau
发明人： James Todhunter , Igor Sovpel , Dzianis Pastanohau
IPC分类号： G06F17/27
CPC分类号： G06F17/279 , G06F17/30654
摘要： Systems and methods for automatic semantic labeling of natural language documents provided in electronic or digital form include a semantic processor that performs a basic linguistic analysis of text, including recognizing in the text semantic relationships of the type objects and/or classes of objects, facts and cause-effect relationships; matching linguistically analyzed text against target semantic relationship patterns, created by generalization of particular cases of target semantic relationships; and generating semantic relationship labels based on linguistically analyzed text and a result of the matching.
摘要翻译： 用于以电子或数字形式提供的自然语言文档的自动语义标注的系统和方法包括执行文本的基本语言分析的语义处理器，包括在文本中识别类型对象和/或对象类别的事实语义关系，事实和 因果关系; 将语言分析的文本与目标语义关系模式进行匹配，由目标语义关系的特定情况的泛化创建; 并且基于语言分析的文本和匹配的结果生成语义关系标签。
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2.发明授权Semantic processor for recognition of whole-part relations in natural language documents 有权用于识别自然语言文档中全部关系的语义处理器公开(公告)号：US08799776B2
公开(公告)日：2014-08-05
申请号：US11686660
申请日：2007-03-15
CPC分类号： G06F17/2785 , G06F17/21 , G06F17/27 , G06F17/30719
摘要： A semantic processor and method for automatically recognizing Whole-Part relations in at least one natural language electronic or digital document recognizes one or more expanded Subject-Action-Object (eSAO) sets in text, wherein each eSAO set has one or more eSAO components; matches the one or more eSAO sets against Whole-Part relationship patterns, and generates one or more eSAO Whole-Part relations based on the matching, wherein the eSAO Whole-Part relation comprises a Whole eSAO and an Part eSAO.
摘要翻译： 用于自动识别至少一种自然语言电子或数字文档中的全部关系的语义处理器和方法识别文本中的一个或多个扩展的主题 - 动作对象（eSAO）集合，其中每个eSAO集合具有一个或多个eSAO组件; 匹配一个或多个eSAO集合对全部关系模式，并且基于匹配产生一个或多个eSAO全部关系，其中eSAO全部关系包括全部eSAO和部分eSAO。
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3.发明申请公开(公告)号：US20060041424A1
公开(公告)日：2006-02-23
申请号：US11258250
申请日：2005-10-24
CPC分类号： G06F17/2785 , G06F17/30719
摘要： A Semantic Processor for the recognition of Cause-Effect relations in natural language documents which includes a Text Preformatter, a Linguistic Analyzer and a Cause-Effect Knowledge Base Generator. The Semantic Processor provides automatic recognition of cause-effect relation both inside single fact and between the facts in arbitrary text documents, where the facts are also automatically extracted from the text in the form of seven-field semantic units. The recognition of Cause-Effect relations is carried out on the basis of linguistic (including semantic) text analysis and a number of recognizing linguistic models built in the form of patterns.
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4.发明授权Question-answering system and method based on semantic labeling of text documents and user questions 有权基于文本文档和用户问题的语义标签的问答系统和方法公开(公告)号：US08666730B2
公开(公告)日：2014-03-04
申请号：US12723449
申请日：2010-03-12
申请人： James Todhunter , Igor Sovpel , Dzianis Pastanohau
发明人： James Todhunter , Igor Sovpel , Dzianis Pastanohau
IPC分类号： G06F17/20 , G06F17/28 , G06F17/27 , G06F17/21 , G10L15/00 , G10L13/00 , G10L13/08 , G10L21/00
CPC分类号： G06F17/279 , G06F17/30654
摘要： A question-answering system for searching exact answers in text documents provided in the electronic or digital form to questions formulated by user in the natural language is based on automatic semantic labeling of text documents and user questions. The system performs semantic labeling with the help of markers in terms of basic knowledge types, their components and attributes, in terms of question types from the predefined classifier for target words, and in terms of components of possible answers. A matching procedure makes use of mentioned types of semantic labels to determine exact answers to questions and present them to the user in the form of fragments of sentences or a newly synthesized phrase in the natural language. Users can independently add new types of questions to the system classifier and develop required linguistic patterns for the system linguistic knowledge base.
摘要翻译： 用于以电子或数字形式提供的文本文档中用于以自然语言提出的问题的精确答案的问答系统基于文本文档和用户问题的自动语义标注。 在基于知识类型的基础知识类型，组成部分和属性的帮助下，根据预定义的目标词分类器的问题类型以及可能的答案的组成部分，系统执行语义标签。 匹配过程利用所提到的语义标签类型来确定问题的确切答案，并以句子片段的形式或自然语言中新合成的短语形式呈现给用户。 用户可以独立地向系统分类器添加新类型的问题，并为系统语言知识库开发所需的语言模式。
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5.发明申请SEMANTIC PROCESSOR FOR RECOGNITION OF WHOLE-PART RELATIONS IN NATURAL LANGUAGE DOCUMENTS 有权用于识别自然语言文件中全部关系的语义处理者公开(公告)号：US20070156393A1
公开(公告)日：2007-07-05
申请号：US11686660
申请日：2007-03-15
IPC分类号： G06F17/27
CPC分类号： G06F17/2785 , G06F17/21 , G06F17/27 , G06F17/30719
摘要： A semantic processor and method for automatically recognizing Whole-Part relations in at least one natural language electronic or digital document recognizes one or more expanded Subject-Action-Object (eSAO) sets in text, wherein each eSAO set has one or more eSAO components; matches the one or more eSAO sets against Whole-Part relationship patterns, and generates one or more eSAO Whole-Part relations based on the matching, wherein the eSAO Whole-Part relation comprises a Whole eSAO and an Part eSAO.
摘要翻译： 用于自动识别至少一种自然语言电子或数字文档中的全部关系的语义处理器和方法识别文本中的一个或多个扩展的主题 - 动作对象（eSAO）集合，其中每个eSAO集合具有一个或多个eSAO组件; 匹配一个或多个eSAO集合对全部关系模式，并且基于匹配产生一个或多个eSAO全部关系，其中eSAO全部关系包括全部eSAO和部分eSAO。
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6.发明授权Semantic processor for recognition of cause-effect relations in natural language documents 有权用于识别自然语言文件中因果关系的语义处理器公开(公告)号：US09009590B2
公开(公告)日：2015-04-14
申请号：US11258250
申请日：2005-10-24
CPC分类号： G06F17/2785 , G06F17/30719
摘要： A Semantic Processor for the recognition of Cause-Effect relations in natural language documents which includes a Text Preformatter, a Linguistic Analyzer and a Cause-Effect Knowledge Base Generator. The Semantic Processor provides automatic recognition of cause-effect relation both inside single fact and between the facts in arbitrary text documents, where the facts are also automatically extracted from the text in the form of seven-field semantic units. The recognition of Cause-Effect relations is carried out on the basis of linguistic (including semantic) text analysis and a number of recognizing linguistic models built in the form of patterns.
摘要翻译： 用于识别自然语言文档中的因果关系的语义处理器，其包括文本预格式化，语言分析器和因果效应知识库生成器。 语义处理器在单个事实之间和任意文本文档中的事实之间提供对因果关系的自动识别，其中事实也以七场语义单位的形式从文本中自动提取。 因果关系的识别是在语言（包括语义）文本分析和一些以模式形式建立的识别语言模型的基础上进行的。
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7.发明授权公开(公告)号：US08583422B2
公开(公告)日：2013-11-12
申请号：US12723472
申请日：2010-03-12
申请人： James Todhunter , Igor Sovpel , Dzianis Pastanohau
发明人： James Todhunter , Igor Sovpel , Dzianis Pastanohau
IPC分类号： G06F17/27
CPC分类号： G06F17/279 , G06F17/30654
摘要： Systems and methods for automatic semantic labeling of natural language documents provided in electronic or digital form include a semantic processor that performs a basic linguistic analysis of text, including recognizing in the text semantic relationships of the type objects and/or classes of objects, facts and cause-effect relationships; matching linguistically analyzed text against target semantic relationship patterns, created by generalization of particular cases of target semantic relationships; and generating semantic relationship labels based on linguistically analyzed text and a result of the matching.
摘要翻译： 用于以电子或数字形式提供的自然语言文档的自动语义标注的系统和方法包括执行文本的基本语言分析的语义处理器，包括在文本中识别类型对象和/或对象类别的事实语义关系，事实和 因果关系; 将语言分析的文本与目标语义关系模式进行匹配，由目标语义关系的特定情况的泛化创建; 并且基于语言分析的文本和匹配的结果生成语义关系标签。
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8.发明申请QUESTION-ANSWERING SYSTEM AND METHOD BASED ON SEMANTIC LABELING OF TEXT DOCUMENTS AND USER QUESTIONS 有权基于文本文档和用户问题的语义标签的问题解答系统和方法公开(公告)号：US20100235164A1
公开(公告)日：2010-09-16
申请号：US12723449
申请日：2010-03-12
申请人： James Todhunter , Igor Sovpel , Dzianis Pastanohau
发明人： James Todhunter , Igor Sovpel , Dzianis Pastanohau
IPC分类号： G06F17/27
CPC分类号： G06F17/279 , G06F17/30654
摘要： A question-answering system for searching exact answers in text documents provided in the electronic or digital form to questions formulated by user in the natural language is based on automatic semantic labeling of text documents and user questions. The system performs semantic labeling with the help of markers in terms of basic knowledge types, their components and attributes, in terms of question types from the predefined classifier for target words, and in terms of components of possible answers. A matching procedure makes use of mentioned types of semantic labels to determine exact answers to questions and present them to the user in the form of fragments of sentences or a newly synthesized phrase in the natural language. Users can independently add new types of questions to the system classifier and develop required linguistic patterns for the system linguistic knowledge base.
摘要翻译： 用于以电子或数字形式提供的文本文档中用于以自然语言提出的问题的精确答案的问答系统基于文本文档和用户问题的自动语义标注。 在基于知识类型的基础知识类型，组成部分和属性的帮助下，根据预定义的目标词分类器的问题类型以及可能的答案的组成部分，系统执行语义标签。 匹配过程利用所提到的语义标签类型来确定问题的确切答案，并以句子片段的形式或自然语言中新合成的短语形式呈现给用户。 用户可以独立地向系统分类器添加新类型的问题，并为系统语言知识库开发所需的语言模式。
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