Papers with large-scale TACRED dataset

1 papers
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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