Paper: Cluster-Specific Named Entity Transliteration

ACL ID H05-1055
Title Cluster-Specific Named Entity Transliteration
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
  • Fei Huang (Carnegie Mellon University, Pittsburgh PA)

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 translit...