Boosting statistical word alignment using labeled and unlabeled data
Hua Ren Wu, Haifeng Wang, Zhanyi Liu · 2006
This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach modifies the supervised boosting algorithm to a semi-supervised learning algorithm by incorporating the unlabeled data. In this algorithm, we build a word aligner by using both the labeled data and the unlabeled data. Then we build a pseudo reference set for the unlabeled data, and calculate the error rate of each word aligner using only the labeled data. Based on this semi-supervised boosting algorithm, we investigate two boosting methods for word alignment. In addition, we improve the word alignment results by combining the results of the two semi-supervised boosting methods. Experimental results on word alignment indicate that semi-supervised boosting achieves relative error reductions of 28.29% and 19.52% as compared with supervised boosting and unsupervised boosting, respectively.