In this paper, we propose an algorithm for identifying each word with its translations in a sentence and translation pair. Previously proposed methods require enormous amounts of bilingual data to train statistical word-by-word translation models. By taking a word-based approach, these methods align frequent words with consistent translations at a high precision rate. However, less frequent words or words with diverse translations generally do not have statistically significant evidence for
confident alignment. Consequently, incomplete or incorrect alignments occur. Here, we attempt to improve on the coverage using class-based rules. An automatic procedure for acquiring such rules is also described. Experimental results confirm that the algorithm can align over 85% of word pairs while maintaining a comparably high precision rate, even when a small corpus is used in training.