Text Data Mining Using Information Extraction

这个研究项目的目标是开发新的算法和系统来有效地从非结构化文本数据中发现知识。该方法首先使用经过训练的信息抽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从这些抽取的数据中发现感兴趣的模式。由于自动从文本中提取的数据具有噪声、异构性和非标准化等特点，该项目研究了两种有效挖掘提取数据的方法。首先，开发了用于归纳仅部分匹配提取的文本的规则的方法。第二，开发了在挖掘之前自动将有噪声的字符串变体聚集到标准化数据项中的方法。还开发了利用发现的知识来进一步提高信息提取的准确性的算法。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。

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.