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

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 rst 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.