Pipelined Biomedical Event Extraction Rivaling Joint Learning
This addresses the problem of efficient and accurate event extraction from biomedical text for researchers, though it is incremental as it improves a specific event type within an existing framework.
The paper tackled biomedical event extraction by proposing a BERT-based n-ary relation extraction method for Binding events, achieving F1 scores of 63.14% and 59.40% on GE11 and GE13 corpora, which allowed a pipelined approach to rival or exceed joint learning methods.
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.