  Motivation: Biomedical event detection is fundamental for information
extraction in molecular biology and biomedical research. The detected events
form the central basis for comprehensive biomedical knowledge fusion,
facilitating the digestion of massive information influx from literature.
Limited by the feature context, the existing event detection models are mostly
applicable for a single task. A general and scalable computational model is
desiderated for biomedical knowledge management. Results: We consider and
propose a bottom-up detection framework to identify the events from recognized
arguments. To capture the relations between the arguments, we trained a
bi-directional Long Short-Term Memory (LSTM) network to model their context
embedding. Leveraging the compositional attributes, we further derived the
candidate samples for training event classifiers. We built our models on the
datasets from BioNLP Shared Task for evaluations. Our method achieved the
average F-scores of 0.81 and 0.92 on BioNLPST-BGI and BioNLPST-BB datasets
respectively. Comparing with 7 state-of-the-art methods, our method nearly
doubled the existing F-score performance (0.92 vs 0.56) on the BioNLPST-BB
dataset. Case studies were conducted to reveal the underlying reasons.
Availability: https://github.com/cskyan/evntextrc
