Date Added: Jul 2012
An important approach to text mining involves the use of natural-language information extraction. Information Extraction (IE) distills structured data or knowledge from un-structured text by identifying references to named entities as well as stated relationships between such entities. IE systems can be used to directly extricate abstract knowledge from a text corpus, or to extract concrete data from a set of documents which can then be further analyzed with traditional data-mining techniques to discover more general patterns. The authors discuss methods and implemented systems for both of these approaches and summarize results on mining real text corpora of biomedical abstracts, job announcements, and product descriptions.