First, we propose a novel model for biomedical event extraction based on MLNs that addresses the aforementioned limitations by leveraging the power of Support Vector Machines (SVMs) (Vapnik, 1995;Joachims, 1999) to handle high-dimensional features.
we (1) learn SVM models using rich linguistic features for trigger and argument detection and type labeling; (2) design an MLN composed of soft formulas (each of which encodes a soft constraint whose associated weight indicates how important it is to satisfy the constraint) and hard formulas (constraints that always need to be satisfied, thus having a weight of ∞) to capture the relational dependencies between triggers and arguments; and (3) encode the SVM output as prior knowledge in the MLN in the form of soft formulas, whose weights are computed using the confidence values generated by the SVMs.