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This paper describes the system for rating the degree of semantic equivalence between two text snippets developed by IHS-RD-Belarus for the SemEval 2016 STS shared task (Task 1). To predict the human ratings of text similarity we use a support vector regression model with multiple features representing similarity and difference scores calculated for each

IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity