In the SVM dual formulation that relies on kernels, i. e. similarity measures between documents, a linear kernel can be interpreted as the number of exact matching n-grams between two documents.
for short documents or when little training data is available, sparsity issues due to word synonymy arise, e. g., the sentences 'John likes hot beverages' and 'John loves warm drinks' have little overlap and therefore low linear kernel value (only 1) in the n-gram feature space, even with dependency tree representations and downward paths for n-grams as illustrated in Figure 1.