Transliteration Extraction from Classical Chinese Buddhist Literature Using Conditional Random Fields with Language Models

Extracting plausible transliterations from historical literature is a key issue in historical linguistics and other research fields. In Chinese historical literature, the characters used to transliterate the same loanword may vary because of different translation eras or different Chinese language preferences among translators. To assist historical linguists and digital humanities researchers, this paper proposes a transliteration extraction method based on the conditional random field method with features based on the language models and the characteristics of the Chinese characters used in transliterations. To evaluate our method, we compiled an evaluation set from two Buddhist texts, the Samyuktagama and the Lotus Sutra. We also constructed a baseline approach with a suffix array based extraction method and phonetic similarity measurement. Our method significantly outperforms the baseline approach, and the method achieves recall of 0.9561 and precision of 0.9444. The results show our method is very effective for extracting transliterations in classical Chinese texts.