SBIR Phase I: Semantic Annotation of Hypertext and its Application in Event Mapping

SBIR第一阶段：超文本语义标注及其在事件映射中的应用

• 批准号: 2050294
• 负责人: Xuchen Yao
• 金额: $ 25.6万
• 依托单位: MAP OS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050294&HistoricalAwards=false
• 关键词: SBIR; Phase; Semantic; Annotation; Hypertext

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to help applications of augmented reality, location-based search, and real-time live mapping of the world, and to build a digital twin of the earth. Event information is dynamic and hyperlocal. Thus it is important to ground the location and time of events to real GPS coordinates and universal time. This creates technical challenges of entity disambiguation and resolution, and real-time processing event information to reflect last-minute changes. The proposed natural language grounding work also helps create a mirrorworld with information interlinked between the virtual internet and physical spaces and events.This Small Business Innovation Research (SBIR) Phase I project will be the first to investigate neural language model pre-training over semi-structured hypertext on a web scale (55TB of compressed data monthly). This will greatly accelerate understanding of noisy web text, while the majority of research to date has been conducted on clean plain text only. This project will also attack the challenge of machine reading with document-level annotations in a semi-supervised fashion, while the predominance of study has been carried out with more precise word-level annotation in a fully supervised way. The technical goal of this project is to create a scalable infrastructure that allows quick iterations of mining web scale data, and an ensemble of algorithms that are adapted to learning structured information over hypertext. It is an unsolved challenge for most small businesses that in the past only the internet giants have attempted. The resulting machine learning algorithms will be capable of interpreting semi-structured web data, in contrast to typical structured annotation to understand hypertext.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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响将是帮助增强现实、基于位置的搜索和实时世界地图的应用，并建立一个地球的数字双胞胎。事件信息是动态的、超本地的。因此，将事件的位置和时间与真实的GPS坐标和世界时接地是很重要的。这给实体消歧和解决以及实时处理事件信息以反映最后一刻的更改带来了技术挑战。拟议的自然语言基础工作也有助于创建一个镜像世界，其中信息在虚拟互联网与物理空间和事件之间相互联系。这个小企业创新研究（SBIR）第一阶段项目将是第一个在网络规模（每月55TB压缩数据）上对半结构化超文本进行神经语言模型预训练的研究。这将大大加快对噪声网络文本的理解，而迄今为止的大多数研究都是在纯文本上进行的。本项目还将以半监督的方式使用文档级注释来解决机器阅读的挑战，同时以全监督的方式使用更精确的单词级注释来进行优势研究。这个项目的技术目标是创建一个可扩展的基础设施，允许快速迭代挖掘网络规模的数据，以及一个适合学习超文本结构化信息的算法集合。对于大多数小企业来说，这是一个尚未解决的挑战，过去只有互联网巨头尝试过。由此产生的机器学习算法将能够解释半结构化的网络数据，而不是典型的结构化注释来理解超文本。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。