Identifying Symptoms and Diseases in MedNLP Japanese Materials Using Chinese Resources

In this paper, we describe the Sinica-Yuntech system (TeamID: SinicaNLP) at the NTCIR-10 MedNLP task. Materials of the MedNLP task are in Japanese. However, having only Chinese resources and knowledge, we need to translate these materials into Chinese. Two preprocessing approaches, different in the timing of translation, were taken. One was to translate Japanese sentences in to Chinese ones, and then to perform segmentation and part of speech tagging on these Chinese sentences; the other was to segment and tag parts of speech on Japanese sentences, and then to translate the composite words. After knowing words and their parts of speech, we identified symptoms and diseases by a vocabulary matching approach. The Internet searching results and parts of speech patterns were also utilized to recognize out of vocabulary symptoms. After recognizing the targets in Chinese, a reverse translation was performed in order to label the original Japanese materials. We merged the tags from vocabulary matching, Internet searching and pattern mapping to obtain the performance of our best run: an f-score 53.88 and an accuracy 91.46.