Korean-Chinese Person Name Translation for Cross Language Information Retrieval

Korean-Chinese Person Name Translation for Cross Language Information Retrieval∗ Yu-Chun Wang ab, Yi-Hsun Lee a, Chu-Cheng Lin ac, Richard Tzong-Han Tsai d*, Wen-Lian Hsu a aInstitute of Information Science, Academia Sinica, Taiwan bDepartment of Electrical Engineering, National Taiwan University, Taiwan cDepartment of Computer Science and Information Engineering, National Taiwan University, Taiwan dDepartment of Computer Science and Engineering, Yuan Ze University, Taiwan {albyu, rog, as1986}@iis.sinica.edu.tw thtsai@saturn.yzu.edu.tw hsu@iis.sinica.edu.tw *corresponding author Abstract. Named entity translation plays an important role in many applications, such as information retrieval and machine translation. In this paper, we focus on translating person names, the most common type of name entity in Korean-Chinese cross language information retrieval (KCIR). Unlike other languages, Chinese uses characters (ideographs), which makes person name translation difficult because one syllable may map to several Chinese characters. We propose an effective hybrid person name translation method to improve the performance of KCIR. First, we use Wikipedia as a translation tool based on the inter-language links between the Korean edition and the Chinese or English editions. Second, we adopt the Naver people search engine to find the query name’s Chinese or English translation. Third, we extract Korean-English transliteration pairs from Google snippets, and then search for the English-Chinese transliteration in the database of Taiwan’s Central News Agency or in Google. The performance of KCIR using our method is over five times better than that of a dictionary-based system. The mean average precision is 0.3490 and the average recall is 0.7534. The method can deal with Chinese, Japanese, Korean, as well as non-CJK person name translation from Korean to Chinese. Hence, it substantially improves the performance of KCIR. Keywords: Person Name Translation, Korean-Chinese Cross Language Information Retrieval 1. Introduction Named entity (NE) translation plays an important role in machine translation, information retrieval, and question answering. It is a particularly challenging task because, although there are many online bilingual dictionaries, they usually lack domain specific words or NEs. Furthermore, new NEs are generated everyday, but the content of bilingual dictionaries cannot be updated frequently. Therefore, it is necessary to construct a named entity translation (NET) system. Economic ties between China and Korea have become closer as China has opened its markets further, and demand for the latest news and information from China continues to grow rapidly in Korea. One key way to meet this demand is to retrieve information written in Chinese by ∗ Copyright 2007 by Yu-Chun Wang, Yi-Hsun Lee, Chu-Cheng Lin, Richard Tzong-Han Tsa, Wen-Lian Hsu 489 using Korean queries, referred to as Korean-Chinese cross-language information retrieval (KCIR). The main challenge involves translating NEs because they are usually the main concepts of queries. In Chen (1998), the authors romanized Chinese NEs and selected their English transliterations from English NEs extracted from the Web by comparing their phonetic similarities with Chinese NEs. Al-Onaizan and Knight (2002) transliterated an NE in Arabic into several candidates in English and ranked the candidates by comparing their occurrences in several English corpora. In the above works, the target languages are alphabetic; however, …

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