Automatic acquisition of bilingual rules for extraction of bilingual word pairs from parallel corpora

In this paper, we propose a new learning method to solve the sparse data problem in automatic extraction of bilingual word pairs from parallel corpora with various languages. Our learning method automatically acquires rules, which are effective to solve the sparse data problem, only from parallel corpora without any bilingual resource (e.g., a bilingual dictionary, machine translation systems) beforehand. We call this method Inductive Chain Learning (ICL). The ICL can limit the search scope for the decision of equivalents. Using ICL, the recall in three systems based on similarity measures improved respectively 8.0, 6.1 and 6.0 percentage points. In addition, the recall value of GIZA++ improved 6.6 percentage points using ICL.