Text mining concerns looking for patterns in unstructured text. The related task of Information Extraction (IE) is about locating specific items in natural-language documents. This paper presents a framework for text mining, called DiscoTEX (Discovery from Text EXtraction), using a learned information extraction system to transform text into more structured data which is then mined for interesting relationships. The initial version of DiscoTEX integrates an IE module acquired by an IE learning system, and a standard rule induction module. In addition, rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of the underlying extraction system. Encouraging results are presented on applying these techniques to a corpus of computer job announcement postings from an Internet newsgroup.

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In Multilingualism and Electronic Language Management: Proceedings of the 4th International MIDP Colloquium, W. Daelemans and T. du Plessis and C. Snyman and L. Teck (Eds.), pp. 141-160, Bloemfontein, South Africa, September 2003. Van Schaik: South Africa.

@InCollection{nahm:midp03, title={Text Mining with Information Extraction}, author={Raymond J. Mooney and Un Yong Nahm}, booktitle={Multilingualism and Electronic Language Management: Proceedings of the 4th International MIDP Colloquium}, month={September}, editor={W. Daelemans and T. du Plessis and C. Snyman and L. Teck}, address={Bloemfontein, South Africa}, publisher={Van Schaik: South Africa}, pages={141-160}, url="http://www.cs.utexas.edu/users/ai-lab?nahm:midp03", year={2003} }