  Causal relation extraction of biomedical entities is one of the most complex
tasks in biomedical text mining, which involves two kinds of information:
entity relations and entity functions. One feasible approach is to take
relation extraction and function detection as two independent sub-tasks.
However, this separate learning method ignores the intrinsic correlation
between them and leads to unsatisfactory performance. In this paper, we propose
a joint learning model, which combines entity relation extraction and entity
function detection to exploit their commonality and capture their
inter-relationship, so as to improve the performance of biomedical causal
relation extraction. Meanwhile, during the model training stage, different
function types in the loss function are assigned different weights.
Specifically, the penalty coefficient for negative function instances increases
to effectively improve the precision of function detection. Experimental
results on the BioCreative-V Track 4 corpus show that our joint learning model
outperforms the separate models in BEL statement extraction, achieving the F1
scores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations,
respectively. This demonstrates that our joint learning system reaches the
state-of-the-art performance in Stage 2 compared with other systems.
