Cross-Lingual Document Clustering

The ever-increasing numbers of Web-accessible documents are available in languages other than English. The management of these heterogeneous document collections has posed a challenge. This paper proposes a novel model, called a domain alignment translation model, to conduct cross-lingual document clustering. While most existing crosslingual document clustering methods make use of an expensive machine translation system to fill the gap between two languages, our model aims to effectively handle the cross-lingual document clustering by learning a cross-lingual domain alignment model and a domain-specific term translation model in a collaborative way. Experimental results show our method, i.e. C-TLS, without any resources other than a bilingual dictionary can achieve comparable performance to the direct machine translation method via a machine translation system, e.g. Google language tool. Also, our method is more efficient.