An important approach to text mining involves the use of natural-language information extraction. Information extraction (IE) distills structured data or knowledge from unstructured 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. We 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. We also discuss challenges that arise when employing current information extraction technology to discover knowledge in text.

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SIGKDD Explorations (special issue on Text Mining and Natural Language Processing), 7(1):3-10, 2005.

@Article{mooney:sigkdd05, title={Mining Knowledge from Text Using Information Extraction}, author={Raymond J. Mooney and R. Bunescu}, volume={7}, journal={SIGKDD Explorations (special issue on Text Mining and Natural Language Processing)}, number={1}, pages={3-10}, url="http://www.cs.utexas.edu/users/ai-lab/pub-view.php?PubID=51469", year={2005} }