In this paper, a system for semantic textual similarity, which participated in Task-1 in SemEval 2016 (monolingual and crosslingual sub-tasks) is described.The system contains a preprocessing step that simplifies text using PPDB 2.0 and detects negations.Also, six lexical similarity functions were constructed using string matching, word embedding and synonyms-antonyms relations in WordNet.These lexical similarity functions are projected to sentence level using a new method called Polarized Soft Cardinality that supports negative similarities between words to model opposites.We also introduce a novel L 2 -norm "cardinality" for vector space representations.The system extracts a set of 660 features from each pair of text snippets using the proposed cardinality measures.From this set, a subset of 12 features was selected in a supervised manner.These features are combined by SVR and, alternatively, by using the arithmetic mean to produce similarity predictions.Our team ranked second in the crosslingual sub-task and got close to the best official results in the monolingual sub-task.