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    • 5. 发明申请
    • GENERATION OF A SEMANTIC MODEL FROM TEXTUAL LISTINGS
    • 文本列表的语义模型的产生
    • US20130262086A1
    • 2013-10-03
    • US13593778
    • 2012-08-24
    • Doo Soon KimPeter Z. YehKunal Verma
    • Doo Soon KimPeter Z. YehKunal Verma
    • G06F17/27
    • G06F17/2785G06F17/2705G06F17/277G06Q30/0256
    • A corpus of textual listings is received and main concept words and attribute words therein are identified via an iterative process of parsing listings and expanding a semantic model. During the parsing phase, the corpus of textual listings is parsed to tag one or more head noun words and/or one or more identifier words in each listing based on previously identified main concept words or using a head noun identification rule. Once substantially each listing in the corpus has been parsed in this manner, the expansion phase assigns head noun words as main concept words and modifier words as attribute words, where possible. During the next iteration, the newly identified main concept words and/or attribute words are used to further parse the listings. These iterations are repeated until a termination condition is reached. Remaining words in the corpus are clustered based on the main concept words and attribute words.
    • 接收到文本列表的语料库,并通过解析列表的迭代过程和扩展语义模型来识别其中的主要概念词和属性词。 在解析阶段期间,解析文本列表的语料库以基于先前识别的主要概念词或使用头名词识别规则来标记每个列表中的一个或多个头名词和/或一个或多个标识符词。 一旦语料库中基本上每个列表已经被这样解析,扩展阶段就可以将头名词作为主要概念词和修饰词分配为属性词。 在下一次迭代中,新确定的主要概念词和/或属性词用于进一步解析列表。 重复这些迭代,直到达到终止条件。 基于主要概念词和属性词,语料库中的剩余单词是聚类的。
    • 8. 发明申请
    • Document Analysis, Commenting, and Reporting System
    • 文件分析,评论和报告制度
    • US20090138793A1
    • 2009-05-28
    • US12121503
    • 2008-05-15
    • Kunal VermaAlex Kass
    • Kunal VermaAlex Kass
    • G06F17/00
    • G06F17/277G06F8/10G06F17/2247G06F17/274G06F17/2775G06F17/30011G06F17/30731
    • A document analysis, commenting, and reporting system provides tools that automate quality assurance analysis tailored to specific document types. As one example, the specific document type may be a requirements specification and the system may tag different parts of requirements, including actors, entities, modes, and a remainder. However, the flexibility of the system permits analysis of any other document type, such as instruction manuals and best practices guides. The system helps avoid confusion over the document when it is delivered because of non-standard terms, ambiguous language, conflicts between document sections, incomplete or inaccurate descriptions, size and complexity of the document, and other issues.
    • 文档分析,评论和报告系统提供了根据特定文档类型自动进行质量保证分析的工具。 作为一个示例,特定文档类型可以是需求规范,并且系统可以标记需求的不同部分,包括角色,实体,模式和余数。 然而,系统的灵活性允许分析任何其他文件类型,如说明手册和最佳做法指南。 该系统由于非标准术语,模糊语言,文档部分之间的冲突,不完整或不准确的描述,文档的大小和复杂性以及其他问题而有助于避免在文档交付时产生混淆。