SBIR Phase I: Automatic Data Series Extraction from a Text Corpus

SBIR 第一阶段：从文本语料库中自动提取数据序列

• 批准号: 2110123
• 负责人: CHIT-KWAN LIN
• 金额: $ 25.6万
• 依托单位: REVELATA, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110123&HistoricalAwards=false
• 关键词: SBIR; Phase; Automatic; Data; Series

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be improved market efficiency and greater competition in financial services and adjacent industries. Currently, these sectors are dominated by large firms that have the resources to create and exploit asymmetric information advantages over smaller firms. A key reason for this asymmetry is that company information – the basis for building high-quality, detailed financial models – can be prohibitively expensive to surface, not because it is unavailable, but because it is reported in non-standardized ways and therefore difficult to extract and make actionable. To do so systematically and industry-wide requires thousands of man-hours per year of manually sifting through millions of documents, a cost that only the largest firms in the world can bear. Automating the extraction of such information and making it both widely available and easily accessible helps to level the playing field for small firms while simultaneously improving the speed and quality of decision-making – at a lower total cost – for large firms.This Small Business Innovation Research (SBIR) Phase I project aims to develop a machine learning platform for automatically extracting data from a collection of financial documents. The platform will take advantage of recent advances in natural language processing model architectures, but nonetheless faces the challenges of (a) achieving and maintaining a high level of accuracy, even as document text volume, and thus semantic variation, grows; and (b) generating sufficient labeled training data in a cost-effective way. This project addresses these dual challenges with a novel framework for continuous model training based on recent meta-learning techniques. Such an approach to supervised learning can substantially accelerate model improvement and simultaneously drive down training costs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这一小型企业创新研究(SBIR)第一阶段项目的更广泛影响将是提高市场效率，以及金融服务业和邻近行业的竞争加剧。目前，这些行业由大公司主导，这些公司拥有创造和利用不对称信息优势的资源，而不是小公司。这种不对称的一个关键原因是，公司信息--构建高质量、详细的财务模型的基础--可能会令人望而却步，这不是因为它不可用，而是因为它是以非标准化的方式报告的，因此很难提取和实施。要系统地在整个行业内做到这一点，每年需要数千个工时的人工筛选数百万份文件，这一成本只有世界上最大的公司才能承担。自动提取此类信息，并使其广泛可用和易于访问，有助于为小公司创造公平的竞争环境，同时提高大公司的决策速度和质量-以较低的总成本。这个小企业创新研究(SBIR)第一阶段项目旨在开发一个机器学习平台，用于自动从一系列财务文档中提取数据。该平台将利用自然语言处理模型架构方面的最新进展，但仍面临以下挑战：(A)即使文件文本数量增加，语义变化也要达到和保持高水平的准确性；(B)以具有成本效益的方式生成足够的标记训练数据。该项目基于最新的元学习技术，通过一个新的持续模型培训框架来解决这些双重挑战。这种监督学习的方法可以大大加快模型改进，同时降低培训成本。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。