Mining Multilingual Texts using Growing Hierarchical Self-Organizing Maps

The WWW provides an ultimate source of information for all kinds of knowledge in various kinds of languages. There are emerging needs for searching documents in different languages, causing multilingual information retrieval an active research topic recently. The performance of such task depends on the degree of understanding for the relationships between different languages. Multilingual text mining aims at discovering interesting relationships between different languages. In this work, we applied the growing hierarchical self-organizing map model to cluster multilingual text documents and find the relationships between two languages. We use a set of parallel corpora to train the map and apply a discovering process to identify the semantic groups and hierarchical structures of keywords for these languages. The discovered knowledge can then be applied to tasks such as multilingual information retrieval and automatic multilingual thesaurus construction.