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1. 发明授权

US07251637B1 Context vector generation and retrieval 有权
标题翻译：上下文矢量生成和检索
公开(公告)号：US07251637B1
公开(公告)日：2007-07-31
申请号：US09672237
申请日：2000-09-27
申请人： William Robert Caid , Joel Lawrence Carleton , Pu Oing , David John Sudbeck
发明人： William Robert Caid , Joel Lawrence Carleton , Pu Oing , David John Sudbeck
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00 , G06N3/02
CPC分类号： G06F17/30265 , G06F17/30256 , G06F17/3061 , G06K9/4623
摘要： A system and method for generating context vectors for use in storage and retrieval of documents and other information items. Context vectors represent conceptual relationships among information items by quantitative means. A neural network operates on a training corpus of records to develop relationship-based context vectors based on word proximity and co-importance using a technique of “windowed co-occurrence”. Relationships among context vectors are deterministic, so that a context vector set has one logical solution, although it may have a plurality of physical solutions. No human knowledge, thesaurus, synonym list, knowledge base, or conceptual hierarchy, is required. Summary vectors of records may be clustered to reduce searching time, by forming a tree of clustered nodes. Once the context vectors are determined, records may be retrieved using a query interface that allows a user to specify content terms, Boolean terms, and/or document feedback. The present invention further facilitates visualization of textual information by translating context vectors into visual and graphical representations. Thus, a user can explore visual representations of meaning, and can apply human visual pattern recognition skills to document searches.
摘要翻译：一种用于生成用于文件和其他信息项的存储和检索的上下文矢量的系统和方法。上下文向量通过定量方式表示信息项之间的概念关系。神经网络使用训练语料库来记录，以使用“窗口共现”技术基于词近似和共同重要性来开发基于关系的上下文向量。上下文向量之间的关系是确定性的，因此上下文向量集具有一个逻辑解，尽管其可以具有多个物理解。不需要人类知识，词库，同义词列表，知识库或概念层次结构。可以通过形成聚类节点树来聚合记录的汇总向量以减少搜索时间。一旦确定了上下文向量，就可以使用允许用户指定内容项，布尔项和/或文档反馈的查询界面来检索记录。本发明通过将上下文矢量转换为视觉和图形表示来进一步促进文本信息的可视化。因此，用户可以探索意义的视觉表示，并且可以应用人类视觉模式识别技能来记录搜索。

2. 发明授权

US5619709A System and method of context vector generation and retrieval 失效
标题翻译：上下文矢量生成和检索的系统和方法
公开(公告)号：US5619709A
公开(公告)日：1997-04-08
申请号：US561167
申请日：1995-11-21
申请人： William R. Caid , Pu Oing
发明人： William R. Caid , Pu Oing
IPC分类号： G06F17/16 , G06F17/30
CPC分类号： G06F17/3069 , G06F17/30265 , G06F17/30696 , Y10S707/99932 , Y10S707/99935
摘要： A system and method for generating context vectors for use in storage and retrieval of documents and other information items. Context vectors represent conceptual relationships among information items by quantitative means. A neural network operates on a training corpus of records to develop relationship-based context vectors based on word proximity and co-importance using a technique of "windowed co-occurrence". Relationships among context vectors are deterministic, so that a context vector set has one logical solution, although it may have a plurality of physical solutions. No human knowledge, thesaurus, synonym list, knowledge base, or conceptual hierarchy, is required. Summary vectors of records may be clustered to reduce searching time, by forming a tree of clustered nodes. Once the context vectors are determined, records may be retrieved using a query interface that allows a user to specify content terms, Boolean terms, and/or document feedback. The present invention further facilitates visualization of textual information by translating context vectors into visual and graphical representations. Thus, a user can explore visual representations of meaning, and can apply human visual pattern recognition skills to document searches.
摘要翻译：一种用于生成用于文件和其他信息项的存储和检索的上下文矢量的系统和方法。上下文向量通过定量方式表示信息项之间的概念关系。神经网络使用训练语料库来记录，以使用“窗口共现”技术基于词近似和共同重要性来开发基于关系的上下文向量。上下文向量之间的关系是确定性的，因此上下文向量集具有一个逻辑解，尽管其可以具有多个物理解。不需要人类知识，词库，同义词列表，知识库或概念层次结构。可以通过形成聚类节点树来聚合记录的汇总向量以减少搜索时间。一旦确定了上下文向量，就可以使用允许用户指定内容项，布尔项和/或文档反馈的查询界面来检索记录。本发明通过将上下文矢量转换为视觉和图形表示来进一步促进文本信息的可视化。因此，用户可以探索意义的视觉表示，并且可以应用人类视觉模式识别技能来记录搜索。

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