Extracting Traceable Formal Models from Natural Language Policy Documents

从自然语言政策文档中提取可追溯的形式模型

• 批准号: 0429948
• 负责人: Insup Lee
• 金额: --
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0429948&HistoricalAwards=false
• 关键词: Extracting; Traceable; Formal; Models; Natural

0429948Insup LeeUniversity of PennsylvaniaExtracting Traceable Formal Models from Natural Language Policy Documents Insup Lee, Aravind JoshiPolicy plays an important role in our lives and affects us in many ways, e.g., the Food and Drug Administration's Code of Federal Regulations govern how to test blood for communicable diseases.Ambiguities, conflicts, and incompleteness in such policy documents could lead to situations that are undesirable and unsafe.The proposed research is to develop NLP (Natural Language Processing) based techniques and methods for extracting formal models from policy documents. These models are then analyzed for correctness andconsistency and also to used for conformance testing of implementations of the policy. This is a collaborative effort between researchers in NLP and Formal Methods and aims at producing an environment in which policy can co-exist in natural and formal languages. For success and usefulness of this approach, it isimportant to maintain correspondence and traceability between these two representations of policy. Furthermore, the large size of the policy bases and the complexity of the documents warrant modularizedextraction of models and then the merging of these models. Existing NLP techniques need to be extended and tailored to aid in the modular extraction of formal models. The merging of extracted models alsorequires extensions and refinements to formal method techniques. As our society relies more on computer-based systems, and on medical devices in particular, the proposed research will help to improve the reliability of such systems.

0429948宾夕法尼亚州的Leeuniversity从自然语言政策文件中提取可追溯的正式模型，使Lee，Aravind Joshipolicy在我们的生活中起着重要的作用，并以许多方式影响我们，例如，粮食和药物管理守则，粮食和药物管理守则在联邦法规中的疾病疾病的疾病范围均可控制疾病。拟议的研究是开发基于NLP（自然语言处理）的技术和方法，用于从政策文件中提取正式模型。 然后，分析这些模型的正确性和一致性，并用于对策略实施的一致性测试。 这是NLP和正式方法研究人员之间的合作努力，旨在产生一个环境，在这种环境中，政策可以使用自然语言和正式语言共存。 对于这种方法的成功和实用性，重要的是在这两种政策表示之间保持对应关系和可追溯性。 此外，策略基础的大尺寸和文档的复杂性值得模块化模块化，然后是这些模型的合并。 现有的NLP技术需要扩展和量身定制，以帮助模块化正式模型。 合并提取的模型Alsorequires扩展和正式方法技术的改进。 随着我们的社会更多地依赖基于计算机的系统，尤其是医疗设备，拟议的研究将有助于提高此类系统的可靠性。