Graph Convolution over Pruned Dependency TreesImproves Relation Extraction
Table 2: F1 scores on SemEval. † marks results reported in the original papers; ‡ marks results produced by using the open implementation. The last two columns show results from with-mention evaluation and mask-mention evaluation, respectively. ∗ marks statistically significant improvements over PA-LSTM with p
['System', '[ITALIC] with-m', '[ITALIC] mask-m']
To study the generalizability of our proposed model, we also trained and evaluated our best C-GCN model on the SemEval test set We find that under the conventional with-entity evaluation, our C-GCN model outperforms all existing dependency-based neural models on this separate dataset. Notably, by properly incorporating off-path information, our model outperforms the previous shortest dependency path-based model (SDP-LSTM). Under the mask-entity evaluation, our C-GCN model also outperforms PA-LSTM by a substantial margin, suggesting its generalizability even when entities are not seen.