Towards Automatic Content-Based Organization of 
                  Multilingual Digital Libraries:
              An English, French and German View of the 
               Russian Information Agency Nowosti News

       Andreas Rauber (1), Michael Dittenbach (2), Dieter Merkl (1)
                 (1) Institut für Softwaretechnik, 
                     Technische Universität Wien
                     Favoritenstraße 9--11/188, 
                     A--1040 Wien, Austria
                 (2) E-Commerce Competence Center -- EC3
                     Siebensterngasse 21/3
                     A--1070 Wien, Austria



In this paper we present the application of theSOMLib  digital library 
system to a multilingual document corpus from the Russian Information 
Agency Novosti. News articles in Russian, English, and German are 
automatically organized into separate topic hierarchies using a novel 
unsupervised neural network, namely the growing hierarchical self-organizing 
map. Furthermore, machine translation is used to create a coherent corpus 
in a single target language. In spite of the noise introduced by the 
automatic translation a consistent topical structuring of the integrated 
document collection can be created by the neural network. This facilitates 
straight-forward browsing and exploration of multilingual document 
collections in a given target language.

Keywords: Document Clustering, Neural Networks, Growing Hierarchical 
 Self-Organizing Map, GHSOM, Machine Translation

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Andreas Rauber, Michael Dittenbach, and Dieter Merkl: Towards Automatic Content-Based Organization of  Multilingual Digital Libraries: An English, French and German View of the  Russian Information Agency Nowosti News.

In: Proceedings of the Third All-Russian Scientific Conference "Digital Libraries: Advanced Methods And Technologies, Digital Collections" (RCDL01)
     Petrozavodsk, Russia,  September 11-13, 2001.