STTR Phase I: SunDIAL: Slot DIscovery And Linking

STTR 第一阶段：SunDIAL：时隙发现和链接

• 批准号: 1549697
• 负责人: James Kukla
• 金额: $ 22.5万
• 依托单位: RedShred
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549697&HistoricalAwards=false
• 关键词: STTR; Phase; SunDIAL; Slot; DIscovery

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project results from it improving the accessibility of documents and providing a powerful tool for consumers and businesses to enable routing, categorizing, and understanding of documents. By producing summaries of opportunity documents for business developers, SunDIAL will accelerate pipeline and business development decisions for businesses of all sizes through automatically generated, SaaS-delivered structured summaries. SunDIAL will advance the ability of practical information extraction systems by generating summaries from any text document. Other broader impacts and benefits include furthering the technology to empower individuals to get the gist quickly of formal documents such as consumer credit contracts, health insurance policies, complex industry request for proposals, grants and other difficult-to-read documents. A successful outcome will allow users to quickly assess documents through infobox displays, triage documents on mobile devices, write rules to sort or filter them automatically, and review key information before ever opening the document.This Small Business Technology Transfer (STTR) Phase I project will discover unsupervised and unrestricted slots and fillers (attributes and values) to construct structured summaries based on keywords and hidden patterns in document collections. SunDIAL goes beyond 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. Furthermore, 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. SunDIAL 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. Linking concepts to a knowledge base provides the additional advantage that the terms can be explicitly mapped to semantic concepts in other ontologies. SunDIAL will lead to advances in knowledge discovery, and advance the methodologies for information retrieval, information extraction, slot filling, and knowledge-base population.

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力来自于它提高了文档的可访问性，并为消费者和企业提供了一个强大的工具，以实现文档的路由，分类和理解。通过为业务开发人员制作机会文档摘要，SunDIAL将通过自动生成的SaaS交付的结构化摘要，为各种规模的企业加速管道和业务开发决策。SunDIAL将通过从任何文本文档生成摘要来提高实用信息提取系统的能力。其他更广泛的影响和好处包括促进技术，使个人能够快速获得正式文件的要点，如消费者信贷合同，健康保险政策，复杂的行业提案要求，赠款和其他难以阅读的文件。一个成功的结果将允许用户通过信息框显示快速评估文档，在移动的设备上对文档进行分类，编写规则来自动排序或过滤文档，并在打开文档之前查看关键信息。这个小企业技术转让（STTR）第一阶段项目将发现无人监督和不受限制的插槽和填充物（属性和值）来基于文档集合中的关键字和隐藏模式构造结构化摘要。Sundial超越了最先进的技术，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。SunDIAL通过消除这些步骤进一步推进了最先进的技术，因为它将识别关键字和知识库概念的链接作为发现过程的第一步。将概念链接到知识库提供了额外的优点，即术语可以显式地映射到其他本体中的语义概念。SunDIAL将推动知识发现的进步，并推进信息检索、信息提取、空位填充和知识库填充的方法。