ITR: Unified Graphical Models of Information Extraction and Data Mining with Application to Social Network Analysis

ITR：信息提取和数据挖掘的统一图形模型及其在社交网络分析中的应用

• 批准号: 0326249
• 负责人: Andrew McCallum
• 金额: $ 294.47万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0326249&HistoricalAwards=false
• 关键词: ITR; Unified; Graphical; Models; Information

This project aims to improve our ability to data mine information previously locked in unstructured natural language text. It focuses on developing novel statistical models for information extraction and data mining that have such tight integration that the boundaries between them disappear---resulting in a powerful unified framework for extraction and mining. Current information extraction methods populate slots in a database by identifying relevant subsequences of text, but they are usually unaware of the emerging patterns and regularities in the database. Current data mining methods begin from a populated database, and they are often unaware of where the data came from, or its inherent uncertainties. The result is that the accuracy of both suffers, and significant mining of complex text sources is beyond reach. This project uses probabilistic graphical models that make extraction and mining decisions in the same probabilistic currency, with a common inference procedure. Such models promise significant gains in accuracy and capability, as well as an opportunity for deeper understanding of the role of high-level, top-down patterns in natural language processing, and the role of low-level, bottom-up language data in symbolic processing. The project grounds this work in two real-world applications domains: scientific research and government information. The extraction and mining of large-scale databases in these domains will have broad impacts by providing useful, constantly-updated Web resources, by enabling insights into government efficiency and the flow of scientific ideas, and by making databases, analyses and source code publicly available.http://kdl.cs.umass.edu/projects/unified-graphical-models.html

该项目旨在提高我们对以前锁定在非结构化自然语言文本中的信息进行数据挖掘的能力。 它专注于开发新的统计模型，用于信息提取和数据挖掘，这些模型具有如此紧密的集成，以至于它们之间的边界消失了-从而形成了一个强大的统一的提取和挖掘框架。 目前的信息提取方法通过识别文本的相关顺序来填充数据库中的槽，但是它们通常不知道数据库中出现的模式和错误。当前的数据挖掘方法开始于填充的数据库，并且它们通常不知道数据来自何处或其固有的不确定性。 结果是两者的准确性都受到影响，并且复杂文本源的显着挖掘是遥不可及的。 该项目使用概率图模型，以相同的概率货币进行提取和挖掘决策，并具有共同的推理过程。 这样的模型有望在准确性和能力方面获得显着的收益，并有机会更深入地理解高级自上而下模式在自然语言处理中的作用，以及低级自下而上语言数据在符号处理中的作用。 该项目将这项工作建立在两个现实世界的应用领域：科学研究和政府信息。 这些领域的大型数据库的提取和挖掘将产生广泛的影响，提供有用的、不断更新的网络资源，使人们能够深入了解政府效率和科学思想的流动，并公开数据库、分析和源代码。available.http://kdl.cs.umass.edu/projects/unified-graphical-models.html