HCC: Small: Semantic Interaction for Visual Text Analytics

HCC：小型：视觉文本分析的语义交互

• 批准号: 1218346
• 负责人: Christopher North
• 金额: $ 50万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218346&HistoricalAwards=false
• 关键词: HCC; Small; Semantic; Interaction; Visual

The goal of this project is to enable the creation of new human-centered computing tools that will help people effectively analyze large collections of textual documents by providing powerful statistical analysis functionality in a usable and intuitive form. To accomplish that, this project investigates "semantic interaction" in visual analytics as a method to combine the large-data computationally-intensive foraging abilities of formal statistical mining algorithms with the intuitive cognitively-intensive sensemaking abilities of human analysts. Semantic interaction enables users to inject their domain expertise into the algorithms by interacting directly with the data. For example, analysts synthesize hypotheses about a set of documents by simply re-organizing them within a spatial visualization, highlighting important sentences, or annotating in the margins. Meanwhile, the underlying statistical models learn from these actions and interactively respond to help spatially organize additional relevant information according to the user's feedback. Intellectual merit: Semantic interaction offers a new approach to interactive visual analytics that emphasizes usability. This research will (1) contribute new user interaction and visual feedback techniques for naturally controlling algorithms via the interactive sensemaking process; (2) contribute a flexible visual analytics framework that seamlessly integrates mathematical models with interactive visualization; and (3) evaluate the effectiveness of semantic interaction, which provides a quantitative mechanism to investigate the complex interplay between human intuition and formal statistical methods.Broader impacts: This research will support a broad range of applications in visual text analytics, including intelligence analysis, funding portfolio management, and literature research. Participating agencies will test the software in ecologically valid settings. The software framework will be distributed to enable others to integrate additional models and to provide a usable platform for analysts. The project also proposes an educational and outreach agenda called "CS for CSI" that exploits the popularity of crime investigation stories to attract young students to technology research.

该项目的目标是创建新的以人为中心的计算工具，通过以可用和直观的形式提供强大的统计分析功能，帮助人们有效地分析大量文本文档。为了实现这一目标，该项目研究了可视化分析中的“语义交互”，将正式统计挖掘算法的大数据计算密集型觅食能力与人类分析师的直观认知密集型感知能力相结合。语义交互使用户能够通过直接与数据交互将其领域专业知识注入算法。例如，分析师通过简单地在空间可视化中重新组织文档，突出重要的句子或在空白处注释来综合关于一组文档的假设。同时，底层统计模型从这些动作中学习，并根据用户的反馈交互式地响应以帮助在空间上组织额外的相关信息。 智力优势：语义交互为强调可用性的交互式可视化分析提供了一种新方法。这项研究将（1）通过交互式意义构建过程，为自然控制算法提供新的用户交互和视觉反馈技术;（2）提供一个灵活的视觉分析框架，将数学模型与交互式可视化无缝集成;（3）评价语义交互的有效性，它提供了一种定量机制来研究人类直觉和正式统计方法之间复杂的相互作用。更广泛的影响：这项研究将支持视觉文本分析的广泛应用，包括情报分析，资金组合管理和文献研究。参与机构将在生态有效的环境中测试软件。将分发软件框架，使其他人能够整合更多的模型，并为分析人员提供一个可用的平台。 该项目还提出了一个名为“犯罪现场调查的CS”的教育和外联议程，利用犯罪调查故事的流行吸引年轻学生进行技术研究。