Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources
Table 2: Results using global constraints.
['[BOLD] BP', 'Score  [BOLD] P', 'Score  [BOLD] R', 'Score  [BOLD] F1', 'Nulls  [BOLD] P', 'Nulls  [BOLD] R', 'Nulls  [BOLD] F1']
Performing one iteration of LBP inference produces our highest performance, an F1 of 44.9. This is 14.9 points higher than Reschke’s best system, and a statistically significant improvement over the unconstrained model (p ≤0.0313).