Modeling Impression in Probabilistic Transliteration into Chinese

For transliterating foreign words into Chi-nese, the pronunciation of a source word is spelled out with Kanji characters. Be-cause Kanji comprises ideograms, an indi-vidual pronunciation may be represented by more than one character. However, because different Kanji characters convey different meanings and impressions, char-acters must be selected carefully. In this paper, we propose a transliteration method that models both pronunciation and im-pression, whereas existing methods do not model impression. Given a source word and impression keywords related to the source word, our method derives possible transliteration candidates and sorts them according to their probability. We evalu-ate our method experimentally.