SBIR Phase I: A New Paradigm for Physical Security Information: A Platform Integrating Social Media and Online News with Information Sharing Across Trusted Networks

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力如下。在商业上，本文描述的技术能够在全球范围内为公司、大学、政府和非政府组织提供更快、更细粒度、更动态的安全信息。该项目使用新颖的方法来改进自然语言处理和机器学习。因此，对有关安全的数字新闻来源进行更好的地理解析，将产生可以以原始方式汇总、显示和分析的风险内容。这使得这些组织的安全管理人员能够更好地了解风险并保护他们的员工，从而提供更高质量的服务。对非政府组织（包括公司）来说，这有助于改进有关业务、旅行和投资的决策。对于政府来说，这可以改善物理安全资源分配。在社会方面，该项目有可能通过改进数据的汇总和可视化，提高安全和安保趋势的透明度和问责制。例如，新兴市场的企业和政府团体可以共同识别以前未被注意到的不安全模式，以支持公共问责制。这个小企业创新研究（SBIR）第一阶段项目在以下方面是对最先进技术的创新。首先，该项目建立在当前地理解析提取方法的基础上，添加了安全和安全领域特有的方法。其次，该项目使用外部数据源进行交叉关联，以提高提取报告的“相关性”和粒度。第三，这个项目利用了组织级用户以及个人用户的贡献。也就是说，这个项目支持一个生态系统的发展，在这个生态系统中，信息的人类用户也对信息的质量、数量和及时性做出了贡献。该贡献还旨在通过机器学习改进地理解析方法。机会在于地理解析提取机制的改进。研究目的是验证NLP算法可以利用安全和保障空间特有的模式的假设；可以利用外部新闻来源来提高粒度和“相关性”分数；用户生成的内容可以支持信息共享的生态系统。预期的结果是，上述创新将导致可用性评分足以满足安全性和安全性用例。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase 1 project is as follows. Commercially, the technology described herein has the capability to provide faster, granular, dynamic information about safety and security, globally, to firms, universities, governments, and NGOs. This project uses novel methods to improve Natural Language Processing and Machine Learning. As a result, better geo-parsing of digital sources of news about security will result in risk content that can be aggregated, displayed, and analyzed in original ways. This enables security managers at these organizations to better understand risk and protect their staff, providing a higher quality of care. For non-governmental organizations (including firms), this enables improved decision-making about operations, travel, and investment. For governments, this enables improved physical security resource allocation. Socially, this project has the potential to improve transparency and accountability regarding trends about safety and security, by improving the aggregation and visualization of data. As an example, groups of firms and governments in emerging markets can collectively identify previously unnoticed patterns of insecurity, in support of public accountability. This Small Business Innovation Research (SBIR) Phase I project is an innovation over the state of the art in the following ways. First, this project builds on current geo-parsing extraction methodologies by adding methodologies unique to the safety and security space. Second, this project uses external data sources for cross correlations to improve the "aboutness" and granularity of extracted reports. Third, this project exploits contributions from users at the organizational level - as well as individuals. That is, this project supports the growth of an ecosystem in which human users of information also contribute to the quality, volume, and timeliness of that information. This contribution is also intended to improve the geo-parsing methodologies via machine learning. The opportunity is the improvement of geo-parsing extraction mechanisms. The research objectives are to test the hypotheses that NLP algorithms can exploit patterns unique to the safety and security space; that external sources of news can be exploited for improved granularity and "aboutness" scores; and that user-generated content can serve to support an ecosystem of information sharing. The anticipated results are that the above innovations will result in usability scoring sufficient for the safety and security use case.