Michael PAUL, Kazuhide YAMAMOTO
Resolution of Referential Expressions
within TDMT
Abstract:This report deals with the processing of contextual phenomena within the framework of
the spoken-language translation system TDMT(Transfer-Driven Machine Translation).
We apply a corpus-based approach to resolve referential expressions in Japanese utterances. In our approach a machine-learning algorithm (decision tree) is utilized to select
automatically the attributes from a tagged training set necessary for the resolution task.
The task-specific decision tree is applied to the input data and the knowledge about the obtained reference objects is used for a context-adopted translation of the source utterances
into English and German.