Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

EMNLP 2018 Yuhao ZhangPeng QiChristopher D. Manning

Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Relation Extraction TACRED GCN F1 64.0 # 13
Relation Extraction TACRED C-GCN F1 66.4 # 11
Relation Extraction TACRED GCN + PA-LSTM F1 67.1 # 9
Relation Extraction TACRED C-GCN + PA-LSTM F1 68.2 # 4

Methods used in the Paper


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