Collaborative Research: SGER: Computer-Assisted Interpretation of Citizen Input in Rebuilding Lower Manhattan

合作研究：SGER：重建曼哈顿下城时公民意见的计算机辅助解释

• 批准号: 0439105
• 负责人: Javed Mostafa
• 金额: --
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0439105&HistoricalAwards=false
• 关键词: Collaborative; Research; SGER; Computer; Assisted

This is a collaborative grant with two PIs; Javed Mostafa of Indiana, and David Stark of Columbia. Intellectual MeritWith a grant from the NSF Digital Government Program, David Stark has been studying the role of information technologies in the public debate surrounding the rebuilding of Lower Manhattan in the wake of the September 11 attacks on the World Trade Center. In the process of conducting that research Stark's team has assembled an extensive digital archive containing 5,000 participant oral statements from one town hall meeting and an additional 19,000 oral statements collected at 240 different venues around New York City in the 'Imagine New York Envisioning Workshops'. These gathered statements provide a rich opportunity for testing various strategies of computer-assisted interpretation because they provide an opportunity to compare the conceptual patterns discerned by human intelligence with findings reached through the analytical methods of artificial intelligence. Supporting the initial explotation of that archive is the purpose of this grant.The technical component of this grant arises from work Javed Mostafa has done under an NSF ITR grant. Data mining research concentrating on spontaneous human conversations is at an early stage of development. Mostafa's approach to data mining can offer different ways to analyze the same data. The project has three specific goals: 1) to detect emergent concepts by applying techniques that do not impose any a priori conditions; 2) to use techniques for analyzing known concepts by applying constraints on the mining process, and 3) to develop visualization of the results to facilitate interpretation by social scientists and support direct validation by citizen participants. Broad Impact Computer mediated communication offers new channels for citizens to express their views to elected officials and government agencies. Often, the resulting deluge of comments poses a technical and political challenge. How can officials/agencies make sense of large-scale citizen input? How can meaningful patterns be efficiently and effectively identified? This project will contribute to advancing understanding of the opportunities and the limitations of computer-assisted interpretation. Its findings will be of considerable interest to scholars as well as to government managers responsible for the rebuilding of lower Manhattan. Summary Many challenges involved in creating new data mining tools demands an interdisciplinary collaboration for access to new data; this project offers such an opportunity. This time-critical testing of artificial intelligence methods will be important in understanding the public input to rebuilding lower Manhattan.

这是一项与两名pi的合作拨款；印第安纳州的贾维德·穆斯塔法和哥伦比亚的大卫·斯塔克。在国家科学基金会数字政府项目的资助下，大卫·斯塔克一直在研究信息技术在围绕9·11世贸中心袭击事件后曼哈顿下城重建的公众辩论中的作用。在进行这项研究的过程中，Stark的团队已经收集了一个广泛的数字档案，其中包括来自一次市政厅会议的5000名参与者的口头陈述，以及在纽约市240个不同地点的“想象纽约设想研讨会”中收集的19,000份口头陈述。这些收集的语句为测试计算机辅助解释的各种策略提供了丰富的机会，因为它们提供了将人类智能识别的概念模式与通过人工智能分析方法获得的结果进行比较的机会。支持对该档案的初步利用是本赠款的目的。这项资助的技术部分来自Javed Mostafa在NSF ITR资助下所做的工作。专注于人类自发对话的数据挖掘研究处于早期发展阶段。穆斯塔法的数据挖掘方法可以提供不同的方法来分析相同的数据。该项目有三个具体目标：1)通过应用不施加任何先验条件的技术来检测突发概念；2)通过对挖掘过程施加约束，使用技术来分析已知概念；3)开发结果的可视化，以方便社会科学家的解释，并支持公民参与者的直接验证。计算机媒介传播为公民向民选官员和政府机构表达意见提供了新的渠道。随之而来的大量评论往往会带来技术和政治上的挑战。官员/机构如何理解大规模的公民投入？如何有效地识别有意义的模式？这个项目将有助于提高对计算机辅助口译的机会和局限性的理解。它的研究结果将引起学者和负责重建曼哈顿下城的政府管理人员的极大兴趣。创建新的数据挖掘工具所面临的许多挑战需要跨学科的协作来获取新数据；这个项目提供了这样一个机会。这种时间紧迫的人工智能方法测试对于理解公众对重建曼哈顿下城的投入非常重要。