Using Information Extraction to Aid the Discovery of Prediction Rules from Text

ABSTRACT Text mining and Information Extra tion (IE) are both topi s of signi ant re ent interest. Text mining on erns applying data mining, a.k.a. knowledge dis overy from databases (KDD) te hniques to unstru tured text. Information extra tion (IE) is a form of shallow text understanding that lo ates spe i pie es of data in natural language do uments, transforming unstru tured text into a stru tured database. This paper des ribes a system alled Dis oTEX, that ombines IE and KDD methods to perform a text mining task, disovering predi tion rules from natural-language orpora. An initial version of Dis oTEX is onstru ted by integrating an IE module based on Rapier and a rule-learning module, Ripper. We present en ouraging results on applying these te hniques to a orpus of omputer job postings from an Internet newsgroup.