The advantage of our approach against such models is twofold: firstly, the word embeddings are accompanied by a generic compositional model that can be used for creating sentence representations independently of any specific task; and secondly, the training is quite forgiving to data sparsity problems that in general a dependency-based approach would intensify (since context words are paired with the grammatical relations they occur with the target word).
a small corpus such as the BNC is sufficient for producing high quality syntax-aware word embeddings.