Learning sequence-to-sequence correspondences from parallel corpora via sequential pattern mining
Kaoru Yamamoto, Taku Kudo, Yuta Tsuboi, Yūji Matsumoto · 2003
We present an unsupervised extraction of sequence-to-sequence correspondences from parallel corpora by sequential pattern mining. The main characteristics of our method are two-fold. First, we propose a systematic way to enumerate all possible translation pair candidates of rigid and gapped sequences without falling into combinatorial explosion. Second, our method uses an efficient data structure and algorithm for calculating frequencies in a contingency table for each translation pair candidate. Our method is empirically evaluated using English-Japanese parallel corpora of 6 million words. Results indicate that it works well for multi-word translations, giving 56--84% accuracy at 19% token coverage and 11% type coverage.