vorgelegt von

Dedicated to my loving parents and to J. (Automatic) machine translation (MT) is one of the most challenging domains in Natural Language Processing (NLP) and plays an important role in ensuring global communica-tion, especially in a multilingual world with access to large amounts of Internet resources. As rule-based MT approaches need manually developed resources, new MT directions have been developed over the last twenty years, such as corpus-based machine transla-tion (CBMT): statistical MT (SMT) and example-based machine translation (EBMT). These new directions are based mainly on the existence of a parallel aligned corpus and, therefore, can be easily employed for lower-resourced languages. In this dissertation we showed how EBMT systems behave when a lower-resourced in-flecting language (i.e. Romanian) is involved in the translation process. For this purpose we built an EBMT baseline system based only on surface forms (the Lin-EBMT sys