Word Alignment Between Chinese and Japanese Using Maximum Weight Matching on Bipartite Graph

The word-aligned bilingual corpus is an important knowledge source for many tasks in NLP especially in machine translation. Among the existing word alignment methods, the unknown word problem, the synonym problem and the global optimization problem are very important factors impacting the recall and precision of alignment results. In this paper, we proposed a word alignment model between Chinese and Japanese which measures similarity in terms of morphological similarity, semantic distance, part of speech and co-occurrence, and matches words by maximum weight matching on bipartite graph. The model can partly solve the problems mentioned above. The model was proved to be effective by experiments. It achieved 80% as F-Score than 72% of GIZA++.