Recently, we are working on Statistical Machine Translation based on hierarchical phrases, Semi-Supervised Machine Learning methods for Natural Language Processing, and Complex Question Answering systems that answer `Why' questions.
Recently, a new approach called ``Statistical Machine Translation'' appeared. This approach analyzes bilingual corpora to get word correspondences or phrase correspondences.
By this approach, we can build a translation system easily. We are promoting the research to get more accurate and more readable translation results.
First, these tools are not satisfactory. When we analyze failures of our Question Answering systems, we often encounter errors of these tools. Therefore, we need more accurate tools.
In order to improve these supervised learning-based systems, we have to prepare huge correctly labeled data. However, we can expect only a small improvement.
Therefore, we are studying ``semi-supervised learning'' because we do not have to prepare more labaled data.