Regarding the phrase kernels, PK generally produced better results than CK, implying that the semantic linearity and ontological relation encoded in the embedding is not sufficient enough and treating them separately is more beneficial.
in this paper, we proposed a novel convolutional sentence kernel based on word embeddings that overcomes the sparsity issue, which arises when classifying short documents or when little training data is available.