BACKGROUND: Extracting relations between bio-entities from biomedical literature 
is often a challenging task and also an essential step towards biomedical 
knowledge expansion. The BioCreative community has organized a shared task to 
evaluate the robustness of the causal relationship extraction algorithms in 
Biological Expression Language (BEL) from biomedical literature.
METHOD: We first map the sentence-level BEL statements in the BC-V training 
corpus to the corresponding text segments, thus generating hierarchically tagged 
training instances. A hierarchical sequence labeling model was afterwards 
induced from these training instances and applied to the test sentences in order 
to construct the BEL statements.
RESULTS: The experimental results on extracting BEL statements from BioCreative 
V Track 4 test corpus show that our method achieves promising performance with 
an overall F-measure of 31.6%. Furthermore, it has the potential to be enhanced 
by adopting more advanced machine learning approaches.
CONCLUSION: We propose a framework for hierarchical relation extraction using 
hierarchical sequence labeling on the instance-level training corpus derived 
from the original sentence-level corpus via word alignment. Its main advantage 
is that we can make full use of the original training corpus to induce the 
sequence labelers and then apply them to the test corpus.