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/商业潜力是跨领域搜索和理解文档。它是消费者和企业路由、分类和确保文档关键点可见的强大工具。这使得不堪重负的用户可以轻松搜索，将主题信息链接到知识库并提取定制数据，即使他们无法想到正确的关键字。 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感兴趣的内容;然而，商业用户并不能从这些功能中受益。用户可以在链接到知识库的摘要中查看概念，从而可以查看摘要，在移动的设备上对文档进行分类，编写规则以自动对文档进行排序或筛选，并在打开文档之前查看关键数据。 这个第二阶段项目通过从复杂的文档中生成应答框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，如消费信贷合同，保险单，行业招标，工程和其他难以阅读的文件。这项创新推进了知识发现、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无监督和不受限制的槽和填充符（属性和值），以基于关键字和文档集合中的底层模式构建结构化摘要。它扩展了现有技术，不需要手动制作插槽目录，并通过发现新插槽来补充监督方法。虽然传统的自然语言处理（NLP）方法对格式良好的句子有效，但本文描述的技术对半结构化内容（如节头、列表和表格）有效。此外，用于事实提取和文本摘要的基于NLP的方法主要是词汇的，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。这种方法通过消除这些步骤进一步推进了现有技术，因为它将识别关键字和到知识库概念的链接作为发现过程中的第一步。这种概念链接使术语能够显式映射到其他本体中的语义概念，以便它们可以用于推理和理解。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.