Improving word alignment by adjusting Chinese word segmentation

Most of the current Chinese word alignment tasks often adopt word segmentation systems firstly to identify words. However, word-mismatching problems exist between languages and will degrade the performance of word alignment. In this paper, we propose two unsupervised methods to adjust word segmentation to make the tokens 1-to-1 mapping as many as possible between the corresponding sentences. The first method is learning affix rules from a bilingual terminology bank. The second method is using the concept of impurity measure motivated by the decision tree. Our experiments showed that both of the adjusting methods improve the performance of word alignment significantly.