Improving Statistical Word Alignment with Ensemble Methods

Abstract. This paper proposes an approach to improve statistical word alignment with ensemble methods. Two ensemble methods are investigated: bagging and cross-validation committees. On these two methods, both weighted voting and unweighted voting are compared under the word alignment task. In addition, we analyze the effect of different sizes of training sets on the bagging method. Experimental results indicate that both bagging and cross-validation committees improve the word alignment results regardless of weighted voting or unweighted voting. Weighted voting performs consistently better than unweighted voting on different sizes of training sets.

• Type: Conference
• Year: 2005
• Where: IJCNLP
• Authors: Hua Wu, Haifeng Wang
• Keywords: IJCNLP 2005, Natural Language Processing, Unweighted Voting, Weighted Voting, Word Alignment