Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

TL;DR

This paper introduces a graph convolutional network extension tailored for relation extraction that efficiently pools information over pruned dependency trees, achieving state-of-the-art results on TACRED.

Contribution

It proposes a novel pruning strategy and a parallelizable graph convolutional model for relation extraction from dependency trees.

Findings

Achieves state-of-the-art performance on TACRED dataset.

Pruning around shortest dependency paths improves relevance of information.

Combining the model with sequence models further enhances results.

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.