Learning to Extract Biological Event and Relation Graphs

This work presents the first machine learning approach for extracting complex relationships, utilizing both a graph kernel and a novel, task-specific feature set, and shows that relationships can be predicted with 77% F-score, or 83% if their type and direction is disregarded.