SBIR Phase I: Authoring Assistance via Contextual Semantic Labeling

SBIR 第一阶段：通过上下文语义标签提供创作协助

• 批准号: 2012993
• 负责人: Steven DeRose
• 金额: $ 21.69万
• 依托单位: DOCUGAMI, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2012993&HistoricalAwards=false
• 关键词: SBIR; Phase; Authoring; Assistance; via

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to advance Natural Language Processing (NLP) to improve productivity, compliance and insight for businesses. Documents are the underlying fabric of business as they hold detailed agreements, obligations, requirements and terms central to business operations with customers, suppliers, partners and regulators. However, documents still represent "dark data", separate and inaccessible to automated business processes. Businesses like commercial real estate, insurance, professional services, financial services, legal firms and many others produce and consume many documents containing similar patterns with innumerable variations. Authoring and executing these agreements is laborious and error-prone, but it is difficult to automate the use of this semi-structured information. This project develops a series of sophisticated steps to discern structure and information from narrative text, applying the latest techniques from several schools of thought in artificial intelligence. This project will enable knowledge workers to gain the assistance of artificial intelligence to author and execute commercial agreements with greater ease, efficiency, precision, confidentiality, compliance and insight.This Small Business Innovation Research (SBIR) Phase I project is to enhance unstructured human-centered text with a structured computer-optimized version, a "shadow" representation of each document that uses XML and database technology to enable innovative software assistance for users and organizations. The research takes a multi-faceted approach, applying computer vision and then creating a pipeline of new algorithms using techniques from Deep Learning, Bayesian, Evolutionary, Symbolic and Classic NLP. The process operates on "small" datasets (10-30 documents) with high accuracy as well as large datasets.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.

小型企业创新研究(SBIR)第一阶段项目的更广泛影响是推进自然语言处理(NLP)，以提高企业的生产率、合规性和洞察力。文档是业务的基础结构，因为它们包含对与客户、供应商、合作伙伴和监管机构的业务运营至关重要的详细协议、义务、要求和条款。然而，文档仍然是“暗数据”，是独立的、自动化业务流程无法访问的。商业房地产、保险、专业服务、金融服务、律师事务所等许多企业都会产生和消费许多包含无数变化的相似模式的文档。编写和执行这些协议既费力又容易出错，但很难使这种半结构化信息的使用自动化。这个项目开发了一系列复杂的步骤，从叙事文本中识别结构和信息，应用了人工智能中几个思想流派的最新技术。该项目将使知识工作者能够获得人工智能的帮助，以更轻松、更高效、更精确、更保密、更合规和更有洞察力地编写和执行商业协议。该小型企业创新研究(SBIR)第一阶段项目旨在通过结构化的计算机优化版本来增强非结构化的以人为中心的文本，即使用XML和数据库技术的每个文档的“影子”表示，以实现为用户和组织提供创新的软件帮助。这项研究采取了多方面的方法，应用计算机视觉，然后使用深度学习、贝叶斯、进化、符号和经典NLP等技术创建了一系列新算法。这一过程以高精度的“小”数据集(10-30个文档)和大型数据集进行操作。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。