Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks

Yuanhe Tian, Guimin Chen, Yan Song, Xiang Wan


Abstract
Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities. However, most existing studies suffer from the noise in the dependency trees, especially when they are automatically generated, so that intensively leveraging dependency information may introduce confusions to relation classification and necessary pruning is of great importance in this task. In this paper, we propose a dependency-driven approach for relation extraction with attentive graph convolutional networks (A-GCN). In this approach, an attention mechanism upon graph convolutional networks is applied to different contextual words in the dependency tree obtained from an off-the-shelf dependency parser, to distinguish the importance of different word dependencies. Consider that dependency types among words also contain important contextual guidance, which is potentially helpful for relation extraction, we also include the type information in A-GCN modeling. Experimental results on two English benchmark datasets demonstrate the effectiveness of our A-GCN, which outperforms previous studies and achieves state-of-the-art performance on both datasets.
Anthology ID:
2021.acl-long.344
Original:
2021.acl-long.344v1
Version 2:
2021.acl-long.344v2
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4458–4471
Language:
URL:
https://aclanthology.org/2021.acl-long.344
DOI:
10.18653/v1/2021.acl-long.344
Bibkey:
Cite (ACL):
Yuanhe Tian, Guimin Chen, Yan Song, and Xiang Wan. 2021. Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4458–4471, Online. Association for Computational Linguistics.
Cite (Informal):
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks (Tian et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.344.pdf
Video:
 https://aclanthology.org/2021.acl-long.344.mp4
Code
 cuhksz-nlp/re-agcn
Data
SemEval-2010 Task 8