Pipelined Biomedical Event Extraction Rivaling Joint Learning
Pengchao Wu Xuefeng Li Jinghang Gu Longhua Qian Guodong Zhou
Biomedical event extraction is an information extraction task to obtain
events from biomedical text, whose targets include the type, the trigger, and
the respective arguments involved in an event. Traditional biomedical event
extraction usually adopts a pipelined approach, which contains trigger
identification, argument role recognition, and finally event construction
either using specific rules or by machine learning. In this paper, we propose
an n-ary relation extraction method based on the BERT pre-training model to
construct Binding events, in order to capture the semantic information about an
event's context and its participants. The experimental results show that our
method achieves promising results on the GE11 and GE13 corpora of the BioNLP
shared task with F1 scores of 63.14% and 59.40%, respectively. It demonstrates
that by significantly improving theperformance of Binding events, the overall
performance of the pipelined event extraction approach or even exceeds those of
current joint learning methods.