Extracting protein-protein interaction (PPI) from biomedical literature is an 
important task in biomedical text mining (BioTM). In this paper, we propose a 
hash subgraph pairwise (HSP) kernel-based approach for this task. The key to the 
novel kernel is to use the hierarchical hash labels to express the structural 
information of subgraphs in a linear time. We apply the graph kernel to compute 
dependency graphs representing the sentence structure for protein-protein 
interaction extraction task, which can efficiently make use of full graph 
structural information, and particularly capture the contiguous topological and 
label information ignored before. We evaluate the proposed approach on five 
publicly available PPI corpora. The experimental results show that our approach 
significantly outperforms all-path kernel approach on all five corpora and 
achieves state-of-the-art performance.