Text Data Mining Using Information Extraction

使用信息提取的文本数据挖掘

• 批准号: 0117308
• 负责人: Raymond Mooney
• 金额: $ 24万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-10-01 至 2005-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0117308&HistoricalAwards=false
• 关键词: Text; Data; Mining; Using; Information

The goal of this research project is to develop new algorithms and systems for effectively discovering knowledge in unstructured textual data. The approach first uses trained information extraction systems to obtain structured data from unstructured natural-language documents or web pages, and then applies rule induction methods to discover interesting patterns in this extracted data. Since data automatically extracted from text is noisy, heterogeneous, and non-standardized, the project studies two approaches to effectively mining extracted data. First, methods are developed for inducing rules that only partially match extracted text. Second, methods are developed that automatically cluster noisy variations of strings into standardized data items prior to mining. Algorithms are also developed for using discovered knowledge to further improve the accuracy of information extraction. Developed methods are being evaluated on large text corpora in business, medicine, science, and technology. The research will contribute to the development of technology capable of automatically discovering significant scientific, commercial, and industrial knowledge from the ever-growing supply of textual electronic information.

该研究项目的目标是开发新的算法和系统，以有效地发现非结构化文本数据中的知识。该方法首先使用经过训练的信息提取系统从非结构化的自然语言文档或网页中获取结构化数据，然后应用规则归纳方法在提取的数据中发现有趣的模式。由于从文本中自动提取的数据是嘈杂的，异构的，非标准化的，该项目研究了两种方法来有效地挖掘提取的数据。首先，方法开发的诱导规则，只有部分匹配提取的文本。其次，开发的方法，自动聚类嘈杂的变化字符串到标准化的数据项挖掘之前。算法也被开发用于使用发现的知识，以进一步提高信息提取的准确性。开发的方法正在商业，医学，科学和技术的大型文本语料库上进行评估。这项研究将有助于开发能够从不断增长的文本电子信息中自动发现重要的科学，商业和工业知识的技术。