In this paper, we study terminology extraction in all its states. We present two terminology extraction tools to compare a knowledge-poor vs. a knowledge-rich approach. Both tools deal with single and multi-word terms and are designed to handle multilingualism. Today they can process six languages, a new language can be added quite rapidly. We present an evaluation run on the six languages and two different domains using crawled comparable corpora, as well as hand-crafted reference term lists. We discuss the three main results for the terminology extraction issue. The first two scenarios concern the knowledge-rich framework. First we compare the performances of the ranking and compare the use of the specificity score vs. the the number of occurrences for all the languages. Secondly we examine the relevancy of the term variant identification to increase the precision ranking whatever the language is. The third evaluation scenario compares the two tools and shows that a probabilistic term extraction approach developed with a minimal effort achieves satisfactory results compared to a rule-based method.