Towards Multilingual Information Discovery through a SOM based Text Mining approach

Text mining has been gaining popularity in the knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. However, currently most text mining tools mainly focus on processing monolingual documents (particularly English documents) only, little attention has been paid to apply the techniques to handle the documents in Asian languages, and further extend the mining algorithms to support the aspects of multilingual information sources. This paper describes our approach for concept discovery from multilingual text collections through a text mining technique. Using a variation of automatic clustering techniques, which applies a neural net approach namely the Self-Organizing Maps (SOM), we have conducted several experiments to uncover associated documents based on Chinese Corpus and Chinese-English bilingual parallel corpora. The initial experiments show some interesting results and a couple of potential ways for future work towards the field of multilingual information discovery.