Named Entity Translation with Web Mining and Transliteration

Long Jiang, Ming Zhou, Lee-Feng Chien, Cheng Niu

This paper presents a novel approach to improve the named entity translation by combining a translite-ration approach with web mining, using web in-formation as a source to complement translitera-tion, and using transliteration information to guide and enhance web mining. A Maximum Entropy model is employed to rank translation candidates by combining pronunciation similarity and bilingual contextual co-occurrence. Experimental results show that our approach effectively improves the precision and recall of the named entity translation by a large margin.