Semantic Role Labeling cannot be performed without an associated linguistic resource. A key resource for such a task is the FrameNet resource based on Fillmores theory of frame semantics. Like many linguistic resources, FrameNet has been built by English native speakers for the English language. To overcome the lack of such resources in other languages, we propose a new approach to FrameNet translation by using bilingual dictionaries and filtering the wrong translations. We define six scores to filter, based on translation redundancy and FrameNet structure. We also present our work on the enrichment of the obtained resource with nouns. This enrichment uses semantic spaces built on syntactical dependencies and a multi-represented k-NN classifier. We evaluate both the tasks on the French language over a subset of ten frames and show improved results compared to the existing French FrameNet. Our final resource contains 15,132 associations lexical units-frames for an estimated precision of 86%.