Ranking is an important task in the field of information retrieval. Ranking may be used in different modules in natural language processing such as search engines. In this paper, we introduce a competitive ranking system which combines three different modules. The system participated in SemEval 2016 question ranking task for the Arabic language. The task is a ranking task that targets reordering results retrieved from search engine. Results reordering is done based on relevancy between search result and the original query issued. The data provided in the competition is in the form of question (query) and 30 question answer pairs retrieved from search engine. For each question retrieved from the search engine the system generates a relevancy score that is to be used for ranking. The proposed system came in the third position in the Competition. Since the majority of modules are unsupervised the unsupervised naming was used.