  This article investigates the use of Transformation-Based Error-Driven
learning for resolving part-of-speech ambiguity in the Greek language. The aim
is not only to study the performance, but also to examine its dependence on
different thematic domains. Results are presented here for two different test
cases: a corpus on "management succession events" and a general-theme corpus.
The two experiments show that the performance of this method does not depend on
the thematic domain of the corpus, and its accuracy for the Greek language is
around 95%.
