In this architecture, the candidate answers are initially extracted and ranked using a shallow candidate retrieval (CR) component that uses solely information retrieval techniques, then they are re-ranked using a "learning to rank" approach.
we used SVM rank 8 , a Support Vector Machines classifier adapted for ranking, and re-ranked the candidate answers with a set of features derived from both the initial CR score and the models we have introduced.