Improved relation classification by deep recurrent neural networks with data augmentation

Yan Xu, Ran Jia, Lili Mou, Ge Li, Yunchuan Chen, Yangyang Lu, Zhi Jin


Abstract
Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with traditional methods. However, existing neural networks for relation classification are usually of shallow architectures (e.g., one-layer convolutional neural networks or recurrent networks). They may fail to explore the potential representation space in different abstraction levels. In this paper, we propose deep recurrent neural networks (DRNNs) for relation classification to tackle this challenge. Further, we propose a data augmentation method by leveraging the directionality of relations. We evaluated our DRNNs on the SemEval-2010 Task 8, and achieve an F1-score of 86.1%, outperforming previous state-of-the-art recorded results.
Anthology ID:
C16-1138
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1461–1470
Language:
URL:
https://aclanthology.org/C16-1138
DOI:
Bibkey:
Cite (ACL):
Yan Xu, Ran Jia, Lili Mou, Ge Li, Yunchuan Chen, Yangyang Lu, and Zhi Jin. 2016. Improved relation classification by deep recurrent neural networks with data augmentation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1461–1470, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Improved relation classification by deep recurrent neural networks with data augmentation (Xu et al., COLING 2016)
Copy Citation:
PDF:
https://aclanthology.org/C16-1138.pdf
Data
SemEval-2010 Task-8