SBIR Phase I: Machine Assisted Comparative Policy Analysis in Public Health

SBIR 第一阶段：公共卫生领域的机器辅助比较政策分析

• 批准号: 1746192
• 负责人: Michael Korostelev
• 金额: $ 22.44万
• 依托单位: Legal Science Partners LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1746192&HistoricalAwards=false
• 关键词: SBIR; Phase; Machine; Assisted; Comparative

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to enhance the capabilities of a research tool for companies, legal experts and researchers undertaking nationwide comparative policy analysis in the public health domain. The tool will assist experts in identifying relevant policy documents by determining and scoring the significance of statutory provisions in context of specific legal questions. The quantitative approach can enable novel policy tracking. Rather than experts setting up alerts for updates to a specific set of documents, this tool learns from the legal text used to answer legal questions to allow for real time tracking and discovery of updates and other relevant documents. This approach to policy tracking can present experts with timely information on updates, along with revealing new documents as they are introduced. Timely analysis can inform policy-makers, facilitating the crafting of optimized evidence-based public health legislation. Reducing the cost and effort of the most time-consuming aspects of legal research can make precise scientific policy analysis affordable and accessible commercially and in real time. This Small Business Innovation Research (SBIR) Phase I project will decrease the time required to produce timely analysis of public health policy across 50 states. This research will apply machine learning, natural language processing and graph theory techniques to extract logical legal ontologies by computing similarities of public health provisions in statutory text. In domain specific problems, large sets of examples of annotated text are required. In the legal domain there is little available expert-labeled legal corpora and purposefully curating this kind of dataset is prohibitively expensive. To address this challenge, the proposed solution integrates transparently into legal experts' workflow while generating ontology that mirrors the approach of a domain expert. The second challenge is that searching for patterns in the relations of a very large network of documents can be very expensive computationally. The proposed solution addresses this by extracting clues from the expert workflow to identify shortcuts that simplify and constrain the larger problem. These clues, combined with sparse expert labeled data can produce a more accurate baseline for optimization of scoring and similarity comparison of larger sets. By being integrated into more workflows, the transparent annotation process and algorithm could be applied to other policy domains.

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力将是增强公司、法律专家和研究人员在公共卫生领域进行全国性比较政策分析的研究工具的能力。该工具将通过确定和评分法律规定在具体法律问题中的重要性，协助专家确定相关政策文件。量化方法可以实现新的政策跟踪。该工具不是由专家为一组特定文件的更新设置警报，而是从用于回答法律问题的法律文本中学习，以便实时跟踪和发现更新和其他相关文件。这种政策跟踪方法可以为专家提供及时的更新信息，并在引入新文档时揭示它们。及时的分析可以为政策制定者提供信息，有助于制定优化的循证公共卫生立法。减少法律研究中最耗时的方面的成本和努力，可以使精确的科学政策分析负担得起，并可在商业上和实时地获得。这个小型企业创新研究(SBIR)第一阶段项目将减少对50个州的公共卫生政策进行及时分析所需的时间。本研究将应用机器学习、自然语言处理和图论技术，通过计算法规文本中公共卫生条款的相似度来提取逻辑法律本体。在领域特定的问题中，需要大量带注释的文本示例。在法律领域，几乎没有专家标记的法律语料库，有目的地管理这类数据集的成本高得令人望而却步。为了应对这一挑战，提出的解决方案透明地集成到法律专家的工作流中，同时生成反映领域专家方法的本体。第二个挑战是，在非常大的文档网络的关系中搜索模式在计算上可能非常昂贵。建议的解决方案通过从专家工作流中提取线索来确定简化和约束更大问题的捷径来解决这一问题。这些线索与稀疏的专家标记数据相结合，可以为更大集合的评分和相似性比较提供更准确的基线。通过集成到更多的工作流中，透明的标注过程和算法可以应用到其他策略领域。