Research Statement - Ronen Feldman

The information age has made it easy to store large amounts of data. The proliferation of documents available on the Web, on corporate intranets, on news wires, and elsewhere is overwhelming. However, while the amount of data available to us is constantly increasing, our ability to absorb and process this information remains constant. Search engines only exacerbate the problem by making more and more documents available in a matter of a few key strokes. Text Mining is a new and exciting research area that tries to solve the information overload problem by using techniques from data mining, machine learning, NLP, IR and knowledge management. Text Mining involves the preprocessing of document collections (text categorization, information extraction, term extraction), the storage of the intermediate representations, the techniques to analyze these intermediate representations (distribution analysis, clustering, trend analysis, association rules etc) and visualization of the results. My research evolves around the various components of text mining. In the following sections I will describe the various research activities that I have done in the recent years and plans for future research. My main motto in research is the combination of theory and practice and indeed in each of the following areas we have developed a complete theory and proved that it actually works in practice by implementing a large scale system based on the theory.