Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (D18-1)
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| Challenge: | Existing dependency-based models neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively. |
| Approach: | They propose an extension of graph convolutional networks that is tailored for relation extraction by pruning dependency trees too aggressively. |
| Outcome: | The proposed model outperforms existing sequence and dependency-based models on the large-scale TACRED dataset and has complementary strengths to sequence models. |
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