This paper describes an approach for handling structural divergences and recovering dropped arguments in an implemented Korean to English machine translation system. The approach relies on canonical predicate-argument structures (or dependency structures), which provide a suitable pivot representation for the handling of structural divergences and the recovery of dropped arguments. It can also be converted to and from the interface representations of many off-the-shelf parsers and generators.

In conventional approaches to Korean analysis, verb subcategorization has generally been used as lexical knowledge. A problem arises, however, when we are given long sentences in which two or more verbs of the same subcategorization are involved. In those sentences, a noun phrase may be taken as the constituent of more than one verb and cause an ambiguity. This paper presents an approach to solving this problem by using structural patterns acquired by a statistical method from corpora. Structural patterns can be the processing units for syntactic analysis and for translation into other languages as well. We have collected 10,686 unique structural patterns from a Korean corpus of 1.27 million words. We have analyzed 2,672 sentences and shown that structural patterns can improve the accuracy of Korean analysis.