SBIR Phase II: SunDIAL: Slot DIscovery And Linking

SBIR 第二阶段：SunDIAL：时隙发现和链接

• 批准号: 1758600
• 负责人: James Kukla
• 金额: $ 74.46万
• 依托单位: RedShred
• 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: 2018-04-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1758600&HistoricalAwards=false
• 关键词: SBIR; Phase; II; SunDIAL; Slot

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to search and understand documents across domains. It is a powerful tool for consumers and businesses to route, categorize and ensure key points of a document are seen. This makes it easy for overwhelmed users to search, link topic information to knowledge bases and extract customized data, even if they can't think of the right keywords. Online search can infer the popularity or relevance of a page based on data from millions of users, including a user's location and what they've shown interest in; however, business users don't benefit from these features. Users view concepts in a summary linked to a knowledge base, allowing them to see summaries, triage documents on mobile devices, write rules to sort or filter them automatically, and see key data before ever opening the document. This Phase II project improves information extraction systems by generating answerboxes from complex documents. Consumers will be able to quickly get the gist of formal documents such as consumer credit contracts, insurance policies, industry solicitations, engineering and other difficult-to-read documents. This innovation advances knowledge discovery, information retrieval methods, information extraction, slot-filling, and knowledge-base population.This Small Business Innovation Research Phase II Project discovers unsupervised and unrestricted slots and fillers (attributes and values) to construct structured summaries based on keywords and underlying patterns in document collections. It extends the state of the art by not requiring a manually crafted catalogue of slots and complements supervised approaches by discovering new slots. While conventional Natural Language Processing (NLP) approaches are effective on well-formed sentences, the techniques described here are effective for semi-structured content such as section headers, lists and tables. Additionally, NLP based approaches for fact extraction and text summarization are primarily lexical, requiring further processing for disambiguating and linking to unique entities and concepts in a knowledge base. This approach further advances the state of the art by eliminating these steps as it identifies keywords and links to knowledge base concepts as a first step in the discovery process. This concept-linking enables terms to explicitly map to semantic concepts in other ontologies such that they are available to enable reasoning and understanding.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)第二阶段项目的更广泛影响/商业潜力是跨域搜索和理解文档。对于消费者和企业来说，它是一种强大的工具，可以对文档进行路由、分类并确保看到文档的关键点。这使得不堪重负的用户很容易进行搜索，将主题信息链接到知识库，并提取定制数据，即使他们想不出正确的关键字。在线搜索可以根据数百万用户的数据推断页面的受欢迎程度或相关性，包括用户的位置和他们表现出的兴趣；然而，商业用户并不能从这些功能中受益。用户可以查看链接到知识库的摘要中的概念，使他们能够在打开文档之前查看摘要、对移动设备上的文档进行分类、编写规则以自动对其进行排序或筛选，以及查看关键数据。该第二阶段项目通过从复杂文件中生成答案框来改进信息提取系统。消费者将能够快速获得消费信贷合同、保险单、行业征集、工程等难以阅读的文件的主旨。这一创新推动了知识发现、信息检索方法、信息提取、空位填充和知识库填充。该小企业创新研究第二阶段项目发现无监督和无限制的空位和填充符(属性和值)，以基于文档集合中的关键字和潜在模式构建结构化摘要。它不需要手动制作插槽目录，并通过发现新插槽来补充受监管的方法，从而扩展了最先进的技术。虽然传统的自然语言处理(NLP)方法对格式良好的句子有效，但这里描述的技术对半结构化内容(如节头、列表和表格)有效。此外，基于自然语言处理的事实提取和文本摘要方法主要是词汇性的，需要进一步处理以消除歧义并链接到知识库中的独特实体和概念。作为发现过程的第一步，该方法通过消除这些步骤来识别关键字和指向知识库概念的链接，从而进一步提高了技术水平。这种概念链接使术语能够明确地映射到其他本体中的语义概念，从而使它们能够进行推理和理解。该奖项反映了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。