Cluster-specific Named Entity Transliteration

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 435–442, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Cluster-specific Named Entity Transliteration Fei Huang School of Computer Science Carnegie Mellon University, Pittsburgh, PA 15213 fhuang@cs.cmu.edu Abstract 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%. 1 Introduction Named Entity (NE) translation and transliteration are very important to many multilingual natural language processing tasks, such as machine trans- lation, crosslingual information retrieval and ques- tion answering. Although some frequently occurring NEs can be reliably translated using in- formation from existing bilingual dictionaries and parallel or monolingual corpora (Al-Onaizan and Knight, 2002; Huang and Vogel, 2002; Lee and Chang, 2003), less frequently occurring NEs, espe- cially new names, still rely on machine translitera- tion to generate their translations. NE machine transliteration generates a phoneti- cally similar equivalent in the target language for a source NE, and transliteration patterns highly de- pend on the name’s origin, e.g., the country or the language family this name is from. For example, when transliterating names 1 from Chinese into English, as shown in the following example, the same Chinese character “金” is transliterated into different English letters according to the origin of each person. 金人庆 --- Jin Renqing (China) 金大中 --- Kim Dae-jung (Korea) 马丁 路德 金 --- Martin Luther King (USA) 金丸信 --- Kanemaru Shin (Japan) 何塞 华金 布伦纳 --- Jose Joaquin Brunner (Chile) Several approaches have been proposed for name transliteration. (Knight and Graehl, 1997) proposed a generative transliteration model to transliterate foreign names in Japanese back to English using finite state transducers. (Stalls and Knight, 1998) expanded that model to Arabic- English transliteration. (Meng et al. 2001) devel- oped an English-Chinese NE transliteration tech- nique using pronunciation lexicon and phonetic mapping rules. (Virga and Khudanpur, 2003) ap- plied statistical machine translation models to “translate” English names into Chinese characters for Mandarin spoken document retrieval. All these approaches exploit a general model for NE trans- literation, where source names from different ori- gins or language families are transliterated into the target language with the same rules …

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