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

这一小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将是提高公司，法律的专家和研究人员在公共卫生领域进行全国比较政策分析的研究工具的能力。该工具将协助专家确定相关政策文件，确定法定条款在具体法律的问题中的重要性并对其进行评分。定量方法可以实现新的政策跟踪。该工具不是由专家为特定文档集的更新设置警报，而是从用于回答法律的问题的法律的文本中学习，以允许对更新和其他相关文档进行真实的跟踪和发现。这种政策跟踪方法可以向专家及时提供有关更新的信息，沿着的是在新文件出台时披露这些文件。 及时的分析可以为决策者提供信息，促进制定最优化的循证公共卫生立法。 减少法律的研究中最耗时方面的费用和努力，可以使精确的科学政策分析在商业上和真实的时间内负担得起和可获得。这个小企业创新研究（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，通过计算法定文本中公共卫生条款的相似度来提取逻辑法律的本体。在特定领域的问题中，需要大量带注释的文本示例。在法律的领域，几乎没有可用的专家标记的法律的语料库，并且有目的地管理这种数据集是非常昂贵的。 为了解决这个问题，所提出的解决方案集成透明到法律的专家的工作流程，同时生成本体，反映了领域专家的方法。第二个挑战是，在一个非常大的文档网络的关系中搜索模式可能是非常昂贵的计算。所提出的解决方案通过从专家工作流程中提取线索来确定简化和约束更大问题的快捷方式来解决这个问题。这些线索与稀疏的专家标记数据相结合，可以产生更准确的基线，用于优化较大集合的评分和相似性比较。通过集成到更多的工作流中，透明的注释过程和算法可以应用于其他策略域。

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.