Korean-Chinese person name translation for cross language information retrieval
|Korean-Chinese person name translation for cross language information retrieval|
|Author(s)||Wang Y.-C., Lee Y.-H., Lin C.-C., Tsai R.T.-H., Hsu W.-L.|
|Published in||PACLIC 21 - The 21st Pacific Asia Conference on Language, Information and Computation, Proceedings|
|Keyword(s)||Korean-Chinese cross language information retrieval, Person name translation (Extra: Chinese characters, Cross language information retrieval, Machine translations, Named entity translation, News agencies, Translation method, Translation tools, Transliteration pairs, Wikipedia, Information retrieval, Search engines, Translation (languages))|
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Korean-Chinese person name translation for cross language information retrieval is a 2007 journal article written in English by Wang Y.-C., Lee Y.-H., Lin C.-C., Tsai R.T.-H., Hsu W.-L. and published in PACLIC 21 - The 21st Pacific Asia Conference on Language, Information and Computation, Proceedings.
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.
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