SBIR Phase II: SunDIAL: Slot DIscovery And Linking

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是搜索和理解跨领域的文档。对于消费者和企业来说，这是一个强大的工具，可以对文档进行路由、分类和确保关键点被看到。这使得不知所措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出页面的受欢迎程度或相关性，包括用户的位置和他们感兴趣的内容；然而，业务用户并不能从这些特性中获益。用户在链接到知识库的摘要中查看概念，允许他们查看摘要，在移动设备上对文档进行分类，编写规则来自动排序或过滤它们，并在打开文档之前查看关键数据。这个第二阶段的项目通过从复杂的文档中生成应答箱来改进信息提取系统。消费者将能够快速获得正式文件的要点，如消费者信贷合同、保险单、行业征求意见稿、工程和其他难以阅读的文件。这一创新促进了知识发现、信息检索方法、信息提取、槽填充和知识库填充。这个小企业创新研究第二阶段项目发现了不受监督和不受限制的槽和填充符（属性和值），以基于文档集合中的关键字和底层模式构建结构化摘要。它通过不需要手动制作老虎机目录来扩展技术状态，并通过发现新老虎机来补充监督方法。虽然传统的自然语言处理（NLP）方法对结构良好的句子有效，但这里描述的技术对半结构化内容（如节标题、列表和表）有效。此外，基于NLP的事实提取和文本摘要方法主要是词法的，需要进一步处理以消除歧义并链接到知识库中的唯一实体和概念。这种方法消除了这些步骤，从而进一步提高了技术水平，因为它将识别关键字和链接到知识库概念作为发现过程的第一步。这种概念链接使术语能够显式地映射到其他本体中的语义概念，这样它们就可以用于推理和理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.