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多项新规定。其中许多规则必须通过一个称为通知和评论(N&A；C)规则制定的过程来创建：该机构起草一项拟议的规则，然后将提案、任何基础数据及其法律和政策理由公开征求公众意见。N&A；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、最昂贵的方法之一。尽管一项拟议的规则可能会收到数十万条评论，但它的法律义务是审查和回应所有重要的评论。随着咨询、研究和/或认证要求的激增，规则制定者发现越来越难跟踪这些要求并认识到哪些规则(如果有的话)与特定规则制定相关。电子规则制定有可能从根本上改变北控流程。它可以使这一过程对公众更加透明和容易获得，并对该机构来说更加可靠和具有成本效益。然而，到目前为止，电子案卷系统和电子规则制定工作台只对可用的技术进行了初步的利用。这项拨款将使用成熟的和新兴的自然语言处理(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、研究和其他辅助文件；以及(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在规则制定期间可能需要分析、咨询或证明。研究小组将与联邦交通部和商务部合作。该小组将特别注重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。评价将涉及：使用公认的自然语言规划执行情况的技术衡量标准(例如，查全率和精确度)；将定性和定量社会科学方法结合起来，以评估机构各级工作人员所认为的将这些工具纳入规则制定过程的情况；以及由受过法律培训的研究人员对规则制定过程进行观察，并由了解规则制定过程的专家进行观察。这项研究将有助于实现电子规则制定的积极潜力，促进自然语言处理领域的最新进展，并加深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方面的专业知识，监管法律和法律信息系统的专业知识，以及技术对组织的影响的社会科学经验-康奈尔团队处于有利地位，能够生成关于关键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。所有数据集和工具都将提供给其他研究人员。要开发的自然语言处理方法是通用技术，可针对任何领域或流派进行培训，在任何需要管理、组织和分析大量文本的环境中都很有用。最后，许多帮助机构规则编写者的相同技术也可以用于DesignAgency网站，这些网站帮助公众在规则制定过程中搜索、分类和以其他方式有选择地访问材料。