Transliteration Extraction from Classical Chinese Buddhist Literature Using Conditional Random Fields

Transliteration Extraction from Classical Chinese Buddhist Literature Using Conditional Random Fields Yu-Chun Wang Department of Computer Science and Information Engineering, National Taiwan University, Taiwan Telecommunication Laboratories, Chunghwa Telecom, Taiwan d97023@csie.ntu.edu.tw Richard Tzong-Han Tsai∗ Department of Computer Science and Information Engineering, National Central University, Zhongli City, Taiwan thtsai@csie.ncu.edu.tw Abstract Extracting plausible transliterations from historical literature is a key issues in his- torical linguistics and other resaech fields. In Chinese historical literature, the charac- ters used to transliterate the same loanword may vary because of different translation eras or different Chinese language prefer- ences among translators. To assist historical linguiatics and digial humanity researchers, this paper propose a transliteration extrac- tion method based on the conditional ran- dom field method with the features based on the characteristics of the Chinese char- acters used in transliterations which are suit- able to identify transliteration characters. To evaluate our method, we compiled an evalu- ation set from the two Buddhist texts, the Samyuktagama and the Lotus Sutra. We also construct a baseline approach with suf- fix array based extraction method and pho- netic similarity measurement. Our method outperforms the baseline approach a lot and the recall of our method achieves 0.9561 and the precision is 0.9444. The results show our method is very effective to extract transliterations in classical Chinese texts. 1 Introduction Cognates and loanwords play important roles in the research of language origins and cultural in- terchange. Therefore, extracting plausible cog- nates or loanwords from historical literature is a key issues in historical linguistics. The adoption of loanwords from other languages is usually through transliteration. In Chinese historical literature, the characters used to transliterate the same loan- word may vary because of different translation eras or different Chinese language/dialect prefer- ences among translators. For example, in classical Chinese Buddhist scriptures, the translation pro- cess of Buddhist scriptures from Sanskrit to classi- cal Chinese occurred mainly from the 1st century to 10th century. In these works, the same San- skrit words may transliterate into different Chi- nese loanword forms. For instance, the surname of the Buddha, Gautama, is transliterated into several different forms such as “瞿曇” (q¨u-tan) or “喬答 摩” (qiao-da-mo), and the name “Culapanthaka” has several different Chinese transliterations such as “朱利槃特” (zhu-li-pan-te) and “周利槃陀 伽” (zhou-li-pan-tuo-qie). In order to assist re- searchers in historical linguistics and other digital humanity research fields, an approach to extract transliterations in classical Chinese texts is neces- sary. Many transliteration extraction methods require a bilingual parallel corpus or text documents con- taining two languages. For example, (Sherif and Kondrak, 2007) proposed a method for learning the string distance measurement function from a sentence-aligned English-Arabic parallel corpus to extract transliteration pairs. (Kuo et al., 2007) proposed a transliteration pair extraction method using a phonetic similarity model. Their approach is based on the general rule that when a new En- glish term is transliterated into Chinese (in modern Chinese texts, e.g. newswire), the English source term usually appears alongside the transliteration. To exploit this pattern, they identify all the …

Content truncated to 500 words