State-of-the-art approaches to ranking in lexical simplification exploit supervised machine learning-based methods that rely mostly on surface features, such as word frequency, word length and n-gram probability, for training the model<cite> (Horn et al., 2014</cite>; Bingel and Søgaard, 2016; Specia, 2016a, 2017) . Moreover, deep architectures are not explored in these models. In this paper, we propose to use a Deep Structured Similarity Model (DSSM) (Huang et al., 2013) to rank substitution candidates.