In this paper we describe our system submitted for evaluation in the CLTE-SemEval-2013 task, which achieved the best results in two of the four data sets, and finished third in average. This system consists of a SVM classifier with features extracted from texts (and their translations SMT) based on a cardinality function. Such function was the soft cardinality. Furthermore, this system was simplified by providing a single model for the 4 pairs of languages obtaining better (unofficial) results than separate models for each language pair. We also evaluated the use of additional circular-pivoting translations achieving results 6.14% above the best official results.