We describe the work carried out by the DCU team on the Semantic Textual Similarity task at SemEval-2015. We learn a regression model to predict a semantic similarity score between a sentence pair. Our system exploits distributional semantics in combination with tried-and-tested features from previous tasks in order to compute sentence similarity. Our team submitted 3 runs for each of the five English test sets. For two of the test sets, belief and headlines, our best system ranked second and fourth out of the 73 submitted systems. Our best submission averaged over all test sets ranked 26 out of the 73 systems.