In this paper we describe our approach to Text Mining by introducing TextMiner. We perform term and event extraction on each document to find features that are likely to have meaning in the domain, and then apply mining on the extracted features labelling each document. The system consists of two major components, the Text Analysis component and the Data Mining component. The Text Analysis component converts semi structured data such as documents into structured data stored in a database. The second component applies data mining techniques on the output of the first component. We apply our approach in the financial domain (financial documents collection) and our main targets are: a) To manage all the available information, for example classify documents in appropriate categories and b) To "mine" the data in order to "discover" useful knowledge. This work is designed to primarily support two languages, i.e. English and Greek. 1.

School for Advanced Studies) of Trieste has developed a new system to acquire and analyze information contained in scientific documentation. Thanks to an EU project called Quasi~E, the group has become one of the high-tech spin-offs in the Area Science Park of Trieste. The aim of the new spin-off is to apply the “know-how ” of the management of scientific data to develop a new system that will analyze patent collections. This new system will be able to organize the patents so that the user will be able to monitor technological trends using information extraction, text-mining, and statistical and clustering analysis. The project is encountering encouragement by small and medium enterprises, because the system provides the tools necessary for such companies to develop and compete in their fields.

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