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This paper explores the hypothesis that semantic relatedness may be more reliably inferred by using a multilingual space, as compared to the typical monolingual representation. Through evaluations using several state-of-the-art semantic relatedness systems, applied on standard datasets, we show that a multilingual approach is better suited for this task, and leads to improvements of up to 47% with respect to the monolingual baseline.