Existing syntactic ordered tree mining methods for extracting characteristic contents from text sets have two problems: 1) subtrees which are semantically the same but are different ordered trees fail to be considered equivalent, and 2) raw extracted subtrees can be difficult to understand. In order to avoid these problems, we have developed a method of transforming all ordered trees so that the ordered trees having the same meaning are considered equivalent. We have also developed a method of constructing Japanese texts from extracted subtrees, and evaluated the effectiveness of our methods as applied to syntactic tree mining.

We wrote this report in Japanese and translated it by NEC's machine translation system PIVOT/JE.) IBS (International Business Service) is the company which does the documentation service which contains translation business. We introduced a machine translation system into translation business in earnest last year. The introduction of a machine translation system changed the form of our translation work. The translation work was divided into some steps and the person who isn't experienced became able to take it of the work of each of translation steps. As a result, a total translation cost reduced. In this paper, first, we report on the usage of our machine translation system. Next, we report on translation quality and the translation cost with a machine translation system. Lastly, we report on the merit which was gotten by introducing machine translation.