Graph Convolution over Pruned Dependency TreesImproves Relation Extraction
Table 3: An ablation study of the best C-GCN model. Scores are median of 5 models.
['Model', 'Dev F1']
We find that: (1) The entity representations and feedforward layers contribute 1.0 F1. (2) When we remove the dependency structure (i.e., setting ~A to I), the score drops by 3.2 F1. (3) F1 drops by 10.3 when we remove the feedforward layers, the LSTM component and the dependency structure altogether. (4) Removing the pruning (i.e., using full trees as input) further hurts the result by another 9.7 F1.