Techniques and Applications for the Processing of Language as Text.
Classification is one of the central issues in any system dealing with text data.
The need for effective approaches is dramatically increased nowadays due to the advent of massive digital libraries containing free-form documents. What we are looking for are powerful methods for the exploration of such libraries whereby the discovery of similarities between groups of text documents is the overall goal. In other words, methods that may be used to gain insight in the inherent structure of the various items contained in a text archive are needed. In this paper we demonstrate the applicability of unsupervised neural networks for the task of text document clustering. Specifically, we describe the results from using self-organizing maps for the exploration of document archives.