SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction
Table 4: Performance of the rule-based model before expert intervention.
['Event Class', 'Recall', 'Precision', 'F1']
The table shows that the overall cost of “snapping to grid” the statistical model is approximately 3 F1 points, which come from a drop in recall. This happens because many feature weights associated with specific labels (such as specific event triggers) have low values (due to sparsity), and, after the discretization process, the model can no longer prioritize these labels over the Nil class. Interestingly, the same process yielded a small increase in precision from 59% to 62%.