Multilingual Spectral Clustering Using Document Similarity Propagation

We present a novel approach for multilingual document clustering using only comparable corpora to achieve cross-lingual semantic interoperability. The method models document collections as weighted graph, and supervisory information is given as sets of must-linked constraints for documents in different languages. Recursive k-nearest neighbor similarity propagation is used to exploit the prior knowledge and merge two language spaces. Spectral method is applied to find the best cuts of the graph. Experimental results show that using limited supervisory information, our method achieves promising clustering results. Furthermore, since the method does not need any language dependent information in the process, our algorithm can be applied to languages in various alphabetical systems.