Structured information plays a critical role in many NLP tasks, such as semantic relation extraction between named entities and semantic role labeling. This paper proposes a principled way to automatically generate constituent structure representation for tree kernel-based protein-protein interaction (PPI) extraction. The main idea behind our approach is that the critical portion in a constituent parse tree for PPI extraction can be automatically determined by the shortest dependency path between the two involved proteins, while other portion can be regarded as noise and ignored safely. Evaluation on multiple PPI corpora shows that our dependency-directed tree kernel-based method achieves promising results. This justifies the effectiveness of tree kernelbased methods for PPI extraction, in particular the advantage of dependency-directed constituent structure representation.
['Longhua Qian', 'Guodong Zhou']
Dependency-directed Tree Kernel-based Protein-Protein Interaction Extraction from Biomedical Literature