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.

联邦监管机构每年都会发布4,000多个新规则。其中许多必须通过称为通知和评论的过程（N＆C）规则制定：该机构起草拟议的规则，然后揭露提案，任何基本数据以及其法律和政策理由的公众评论。 N＆C规则制定是当代公共政策制定最重要的方法之一；它也是最慢，最昂贵的之一。尽管代理机构可能会收到数十万条评论，以便为拟议的规则收到其法律义务，是审查和回应所有重要评论。作为咨询，研究和/或认证的要求，规则作者发现，跟踪它们并认识到哪种（如果有的话）与特定的规则制定相关，这越来越困难。电子规则制定（搜索）具有从根本上转化N＆C工艺的潜力。它可以使该过程更加透明，公众可以访问，并且对该机构更可靠和成本效益。不过，到目前为止，E-wocking Systems和Erulemaking Workbenches仅对可用技术进行基本的使用。本赠款将使用自然语言处理的发达和新兴方法（NLP）开发工具来帮助代理机构规则作者：（1）组织，分析和管理与拟议规则相关的评论，研究和其他支持文档； （2）分析拟议的规则，以在大量法规和行政命令中提出可能需要的法律规定，这些法规和执行命令可能需要规则制定期间要求，咨询或证书。研究团队将与联邦运输和商业部门合作。 该团队将尤其将重点放在使用信息提取，文本分类和面向意见的文本分析技术上，并在受监督和弱监督的机器学习框架中。评估将涉及：使用公认的NLP绩效技术度量（例如，回忆和精度）；定性和定量社会科学方法的结合，以评估工具将工具集成到法规制定过程中，这是由员工在代理层次结构的各个层面上所感知的；以及经过法律训练的研究人员的观察，对规则制定过程的专家了解。这项研究将有助于实现磨难，推进NLP的最先进的积极潜力，并提高我们对技术对规则制定影响的理解。由于其跨学科的组成 - 结合了NLP的专业知识，有关监管法和法律信息系统的专家知识以及社会科学在技术对组织的影响方面的经验 - 康奈尔团队非常适合生成有关定性和定量数据，既有定性和定量数据涉及有关至关重要的，但仍在研究的过程中，统治者的phorders and PHREDS。康奈尔信息科学课程的本科生。所有数据集和工具都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的训练，并且在任何需要管理，组织和分析大量文本的情况下有用。最后，许多相同的技术可以帮助代理商规则作者来设计帮助公众搜索，分类或以其他方式选择性地访问规则制定过程中的材料的网站。