Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

Yuhao Zhang, Peng Qi, Christopher D. Manning

Abstract
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. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art.
Anthology ID:
D18-1244
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2205–2215
Language:
URL:
https://aclanthology.org/D18-1244
DOI:
10.18653/v1/D18-1244
Bibkey:
Cite (ACL):
Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018. Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2205–2215, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (Zhang et al., EMNLP 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/D18-1244.pdf
Attachment:
 D18-1244.Attachment.pdf
Code
 qipeng/gcn-over-pruned-trees
Data
Re-TACRED, TACRED
PDF Search Code Attachment