EAGER: Network Pattern Recognition Project

EAGER：网络模式识别项目

• 批准号: 0970179
• 负责人: Lewis Lancaster
• 金额: $ 30万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0970179&HistoricalAwards=false
• 关键词: EAGER; Network; Pattern; Recognition; Project

This project proposes to explore integrated methodologies for pattern discovery and network analysis in multi-lingual text corpora. The interdisciplinary project team will focus on recent developments in network analysis of complex problems associated with visual query systems, topic discovery, anomaly detection, and rapid mining of complex time-stamped data as a means for extending approaches to noisy data from a range of disciplinary source materials. In order to look at the various problems and solutions to these major issues in current scholarship, the PIs have chosen three sets of disparate data: Buddhist Canonic texts (Chinese and Sanskrit); Irish studies journals (English and Gaelic); and Danish folklore (English and Danish). The research exercise to be performed is of a scale and complexity never before attempted. The project anticipates finding deficiencies in existing network analysis algorithms dealing with rich external data available on nodes and links and developing new network analysis algorithms to overcome the deficiencies.

本计画旨在探讨多语言文本语料库模式发现与网路分析的整合方法。跨学科项目团队将专注于与视觉查询系统，主题发现，异常检测和复杂的时间戳数据的快速挖掘相关的复杂问题的网络分析的最新发展，作为从一系列学科源材料中扩展噪声数据方法的手段。 为了研究当前学术界存在的各种问题和解决这些主要问题的方法，PI选择了三组不同的数据：佛教经典文本（中文和梵文）;爱尔兰研究期刊（英语和盖尔语）;和丹麦民俗（英语和丹麦语）。要进行的研究工作的规模和复杂性是前所未有的。 该项目预计将发现现有网络分析算法在处理节点和链路上丰富的外部数据方面的不足，并开发新的网络分析算法来克服这些不足。