The Transliteration from Alphabet Queries to Japanese Product Names

There are some cases where the nonJapanese buyers are unable to find products they want through the Japanese shopping Web sites because they require Japanese queries. We propose to transliterate the inputs of the non-Japanese user, i.e., search queries written in English alphabets, into Japanese Katakana to solve this problem. In this research, the pairs of the nonJapanese search query which failed to get the right match obtained from a Japanese shopping website and its transcribed word given by volunteers were used for the training data. Since this corpus includes some noise for transliteration such as the free translation, we used two different filters to filter out the query pairs that are not transliterlated in order to improve the quality of the training data. In addition, we compared three methods, BIGRAM, HMM, and CRF, using these data to investigate which is the best for the query transliteration. The experiment revealed that the HMM was the best.