USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a Box

In this paper we describe our participa-tion in the STS Core subtask which is the determination of the monolingual seman-tic similarity between pair of sentences. In our participation we adapted state-of-the-art approaches from related work ap-plied on previous STS Core subtasks and run them on the 2016 data. We inves-tigated the performance of single meth-ods but also the combination of them. Our results show that Convolutional Neu-ral Networks (CNN) are superior to both the Monolingual Word Alignment and the Word2Vec approaches. The combination of all the three methods performs slightly better than using CNN only. Our results also show that the performance of our systems varies between the datasets.