Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique

Experimental results showing that the semantic hierarchies generated by the method yield learned translation rules with higher average accuracy are reported.

Abstract

This paper addresses the problem of constructing a semantic hierarchy for a machine translation system. We propose two methods of constructing a hierarchy: acquiring a hierarchy from scratch and updating a hierarchy. When acquiring a hierarchy from scratch, translation rules are learned by an inductive learning algorithm in the first step. A new hierarchy is then generated by applying a clustering method to internal disjunctions of the learned rules and new rules are learned under the bias of this hierarchy. When updating an existing manually-constructed hierarchy, we take advantage of its node structure. We report experimental results showing that the semantic hierarchies generated by our method yield learned translation rules with higher average accuracy.