Semi-supervised learning for word sense disambiguation using parallel corpora

The Application of word sense disambiguation (WSD) methods based on supervised machine learning are limited by the difficulties in defining sense tags and acquiring labeled data for training. In this paper, the two problems of WSD are solved in a semi-supervised learning framework with the help of parallel corpora. The sense tags are defined automatically according to the results of word alignment on the parallel corpora. And label propagation, a graph-based semi-supervised algorithm, is employed. The experiments show that our method achieves great improvement on Chinese WSD tasks and the performances get significant growth when the scale of monolingual sentences is increasing.