SBIR Phase I: Semantic Information Extraction From Text

SBIR 第一阶段：从文本中提取语义信息

• 批准号: 1820118
• 负责人: Jiang Zhou
• 金额: $ 22.5万
• 依托单位: AI STRIKE, LLC
• 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: 2018-07-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1820118&HistoricalAwards=false
• 关键词: SBIR; Phase; Semantic; Information; Extraction

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project can be summarized as follows. (1) Businesses will benefit from the proposed product because companies are increasingly using data-driven predictive models to improve their bottom line. In many areas where structured data are readily available, such as credit scoring, these models have produced impressive returns on investment. One of the main obstacles hindering the application of these models in other areas is the lack of structured data. By extracting information from sources such as web pages, blogs and social media messages, and storing it as structured data, companies will be able to take advantage of the vast amount of unstructured data that are generated daily and thereby improve their bottom line. (2) As the Chinese economy expands and becomes more deeply intertwined with the US economy, many US businesses will need high-quality and timely information about Chinese markets. However, information extraction from Chinese text is an underserved area. The proposed product can fill this void and thereby meet a significant commercial demand.This Small Business Innovation Research (SBIR) Phase I project will develop a new information extraction (IE) method. Currently IE focuses on information extraction from short text snippets consisting of a few words in order to derive structured factual information from unstructured text. But its performance is often deteriorated by the shortage of features - the sparse feature problem. A major benefit of the approach developed in this project is that it takes advantage of the important role of specific linguistic units even when the number of words in a sentence is limited. These linguistic units give a sentence its structure. Depending on structural characteristics or functional principles of sentences, the features around them are grouped. The grouped features satisfy certain properties and can be used to capture structural and semantic information, which is helpful for minimizing the sparse feature problem.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的影响/商业潜力可以总结如下。(1)企业将从拟议的产品中受益，因为公司越来越多地使用数据驱动的预测模型来提高他们的底线。在结构化数据容易获得的许多领域，如信用评分，这些模型产生了令人印象深刻的投资回报。阻碍这些模型在其他领域应用的主要障碍之一是缺乏结构化数据。通过从网页、博客和社交媒体消息等来源中提取信息，并将其存储为结构化数据，企业将能够利用每天生成的大量非结构化数据，从而提高其利润。(2)随着中国经济的扩张，并与美国经济更加紧密地联系在一起，许多美国企业将需要有关中国市场的高质量和及时的信息。然而，从中文文本中提取信息是一个不足的领域。这项小型企业创新研究（SBIR）第一期项目将开发一种新的信息提取（IE）方法。目前IE专注于从由几个单词组成的短文本片段中提取信息，以便从非结构化文本中获得结构化的事实信息。但它的性能往往恶化的不足的特征-稀疏特征的问题。本项目开发的方法的一个主要好处是，即使在句子中的单词数量有限的情况下，它也利用了特定语言单位的重要作用。这些语言单位赋予句子结构。根据句子的结构特征或功能原则，围绕它们的特征被分组。该奖项反映了NSF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。