Natural Language Processing Support for eRulemaking

对电子规则制定的自然语言处理支持

• 批准号: 0535099
• 负责人: Claire Cardie
• 金额: $ 50万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-11-15 至 2010-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0535099&HistoricalAwards=false
• 关键词: Natural; Language; Processing; Support; eRulemaking

Each year Federal regulatory agencies issue more than 4,000 new rules. Many of these must be created through a process known as notice and comment (N&C) rulemaking: the agency drafts a proposed rule and then exposes the proposal, any underlying data, and its legal and policy rationale to public comment. N&C rulemaking is one of the most important methods of contemporary public policy making; it is also one of the slowest and most expensive. Although an agency may receive hundreds of thousands of comments for a proposed rule, its legal obligation is to review and respond to all significant comments. As requirements to consult, study, and/or certify have proliferated, rule writers have found it increasingly difficult to keep track of them and to recognize which, if any, are relevant in a particular rulemaking. Electronic rulemaking (eRulemaking) has the potential to radically transform the N&C process. It could make the process more transparent and accessible to the public, and more substantively reliable and cost-effective for the agency. So far, though, E-docket systems and eRulemaking workbenches make only rudimentary use of available technology.This grant will use well-developed and emerging methods of natural language processing (NLP) to develop tools to aid agency rule writers in: (1) organizing, analyzing, and managing the comments, studies, and other supporting documents associated with a proposed rule; and (2) analyzing proposed rules to flag possibly relevant legal mandates from among the large number of statutes and Executive Orders that potentially requireanalyses, consultations, or certifications during rulemaking. The research team will collaborate with the Federal Departments of Transportation and Commerce. The team will focus, in particular, on the use ofinformation extraction, text categorization, and opinion-oriented text analysis techniques in both supervised and weakly supervised machine learning frameworks. Evaluation will involve: the use of accepted technical measures of NLP performance (e.g., recall and precision); a combination of qualitative and quantitative social science methods to assess integration of the tools into the rulewriting process as perceived by staff at various levels of the agency hierarchy; and observation by legally-trained researchers with expert understanding of the rulemaking process.Intellectual Merit. The research will help realize the positive potential of eRulemaking, advance the state-of-the-art in NLP, and improve our understanding of the effects of technology on rulemaking. Because of its interdisciplinary composition - combining expertise in NLP, expert knowledge about regulatory law and legal information systems, and social science experience in the effect of technology on organizations - the Cornell team is well situated to generate both qualitative and quantitative data about the crucial, but stilllargely under-studied, rulemaking process.Broader Impacts.The project provides an important opportunity for interdisciplinary education and research for PhD, master's, and undergraduate students in Cornell's Information Science Program. All data sets and tools will be made available to other researchers. The NLP methods to be developed are general-purpose techniques, trainable for any domain or genre, and useful in any context that requires managing, organizing, and analyzing large volumes of text. Finally, many of the same techniques that help agency rule writers can be used to designagency websites that help the public search, sort, and otherwise selectively access materials in the rulemaking process.

联邦监管机构每年发布4000多条新规定。其中许多必须通过一个被称为通知和评论（N&amp；C）规则制定的过程来创建：机构起草一项拟议规则，然后将提案、任何基础数据及其法律和政策依据公开征求公众意见。规则制定是当代公共政策制定的重要手段之一；它也是最慢、最昂贵的火车之一。虽然一个机构可能会收到成千上万条关于拟议规则的评论，但它的法律义务是审查并回应所有重要的评论。随着咨询、研究和/或认证需求的激增，规则编写者发现越来越难以跟踪这些需求，并识别哪些（如果有的话）与特定规则制定相关。电子规则制定（eRulemaking）具有从根本上改变N&amp；C过程的潜力。它可以使这一过程更加透明和便于公众使用，并使该机构在实质上更加可靠和具有成本效益。然而，到目前为止，电子摘要系统和规则制定工作台仅对现有技术进行了基本的利用。该拨款将使用成熟的新兴自然语言处理（NLP）方法来开发工具，以帮助机构规则编写者：(1)组织、分析和管理与拟议规则相关的评论、研究和其他支持文件；(2)分析拟议规则，从大量法规和行政命令中标记可能相关的法律授权，这些法规和行政命令可能需要在规则制定过程中进行分析、咨询或认证。该研究小组将与联邦运输部和商务部合作。该团队将特别关注在监督和弱监督机器学习框架中使用信息提取、文本分类和面向意见的文本分析技术。评估将涉及：使用公认的NLP性能技术措施（例如，召回率和准确性）；结合定性和定量的社会科学方法，以评估机构各级工作人员对这些工具融入规则编写过程的看法；由受过法律训练的研究人员进行观察，他们对规则制定过程有专业的了解。知识价值。该研究将有助于实现规则制定的积极潜力，推动NLP的最新发展，并提高我们对技术对规则制定的影响的理解。由于其跨学科的组成——结合了自然语言处理方面的专业知识、监管法律和法律信息系统方面的专业知识，以及技术对组织的影响方面的社会科学经验——康奈尔大学的团队能够很好地生成关于关键的、但在很大程度上仍未得到充分研究的规则制定过程的定性和定量数据。更广泛的影响。该项目为康奈尔大学信息科学专业的博士、硕士和本科生提供了跨学科教育和研究的重要机会。所有数据集和工具将提供给其他研究人员。要开发的NLP方法是通用技术，可用于任何领域或类型，并且在需要管理、组织和分析大量文本的任何上下文中都很有用。最后，许多帮助机构规则编写者的相同技术可以用于设计机构网站，以帮助公众在规则制定过程中搜索、排序和有选择地访问材料。