A major challenge in text mining for biomedicine is automatically extracting 
protein-protein interactions from the vast amount of biomedical literature. We 
have constructed an information extraction system based on the Hidden Vector 
State (HVS) model for protein-protein interactions. The HVS model can be trained 
using only lightly annotated data whilst simultaneously retaining sufficient 
ability to capture the hierarchical structure. When applied in extracting 
protein-protein interactions, we found that it performed better than other 
established statistical methods and achieved 61.5% in F-score with balanced 
recall and precision values. Moreover, the statistical nature of the pure 
data-driven HVS model makes it intrinsically robust and it can be easily adapted 
to other domains.