This paper describes a method for retrieving technical terms and finding their translation candidates from patent corpora. The method improves the reliability of bilingual seed words that measure similarity between a target word and its translation candidates. We conducted an experiment with PAJ (Patent Abstracts of Japan), which is a collection of bilingual patent abstracts written in Japanese and English. The experiment result shows that our method achieves a precision of 53.5% and a recall of 75.4%.

This paper describes a comprehensive translation environment build on the Internet. This environment is designed not only to translate web pages but also to support translation work on the web. We first introduce a basic idea and implementation of this environment and then compare it to conventional machine translation (MT) systems available on the web and translation memories.

This paper describes a new version of our machine translation system PENSÉE. In the light of its past systems, new PENSÉE is designed to improve portability from developers' point of view and translation quality from users' point of view. The features of new PENSÉE are: 1) Java implementation and 2) pattern-based transfer approach. In addition, new PENSÉE places a great importance on user interface especially in building user dictionaries. We will discuss why and how we resolve the existing MT problems and present dictionary building tools to support user customization.