Cluster-specific named entity transliteration

Existing named entity (NE) transliteration approaches often exploit a general model to transliterate NEs, regardless of their origins. As a result, both a Chinese name and a French name (assuming it is already trans-lated into Chinese) will be translated into English using the same model, which often leads to unsatisfactory performance. In this paper we propose a cluster-specific NE transliteration framework. We group name origins into a smaller number of clusters, then train transliteration and language mod-els for each cluster under a statistical ma-chine translation framework. Given a source NE, we first select appropriate models by classifying it into the most likely cluster, then we transliterate this NE with the corre-sponding models. We also propose a phrase-based name transliteration model, which ef-fectively combines context information for transliteration. Our experiments showed substantial improvement on the translitera-tion accuracy over a state-of-the-art baseline system, significantly reducing the transliteration character error rate from 50.29 % to 12.84%.