The BioCreative-V community proposed a challenging task of automatic extraction 
of causal relation network in Biological Expression Language (BEL) from the 
biomedical literature. Previous studies on this task largely used models induced 
from other related tasks and then transformed intermediate structures to BEL 
statements, which left the given training corpus unexplored. To make full use of 
the BEL training corpus, in this work, we propose a deep learning-based approach 
to extract BEL statements. Specifically, we decompose the problem into two 
subtasks: entity relation extraction and entity function detection. First, two 
attention-based bidirectional long short-term memory networks models are used to 
extract entity relation and entity function, respectively. Then entity relation 
and their functions are combined into a BEL statement. In order to boost the 
overall performance, a strategy of threshold filtering is applied to improve the 
precision of identified entity functions. We evaluate our approach on the 
BioCreative-V Track 4 corpus with or without gold entities. The experimental 
results show that our method achieves the state-of-the-art performance with an 
overall F1-measure of 46.9% in stage 2 and 21.3% in stage 1, respectively.