Context awareness and embedding for biomedical event extraction
Table 8: Performance comparison between VeComNet and the best method (Uturku) on BioNLPST-BGI dataset
['Method Event Type', 'VeComNet Precision', 'VeComNet Recall', 'VeComNet F-score', 'Uturku  Precision', 'Uturku  Recall', 'Uturku  F-score']
We compared our performance with that of the best method in the competition on BioNLPST-BGI dataset with respect to each event type. VeComNet performs the best on “Interaction”, “RegulonMember”, “SiteOf”, “TranscriptionBy” events with significant improvement on the F-scores (0.12, 0.32, 0.68, 0.4) compared to the best existing approach; and has competitive performance on “RegulonDependence” and “TranscriptionFrom” events. The performance of VeComNet on other events are stable and impressive due to which its average performance is better than the Uturku’s approach. The compared method from Uturku seems over-fitting to the dataset since in most of the event types it achieved the ideal F-scores of 1.0 where our proposed method does not. Our method outstands from other approaches according to its generalization ability instead of the ideal F-scores. But the deep learning model adopted in VeComNet is limited by the number of training samples.