Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text

Ji Wen, Xu Sun, Xuancheng Ren, Qi Su


Abstract
Relation classification is an important semantic processing task in the field of natural language processing. In this paper, we propose the task of relation classification for Chinese literature text. A new dataset of Chinese literature text is constructed to facilitate the study in this task. We present a novel model, named Structure Regularized Bidirectional Recurrent Convolutional Neural Network (SR-BRCNN), to identify the relation between entities. The proposed model learns relation representations along the shortest dependency path (SDP) extracted from the structure regularized dependency tree, which has the benefits of reducing the complexity of the whole model. Experimental results show that the proposed method significantly improves the F1 score by 10.3, and outperforms the state-of-the-art approaches on Chinese literature text.
Anthology ID:
N18-2059
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
365–370
Language:
URL:
https://aclanthology.org/N18-2059
DOI:
10.18653/v1/N18-2059
Bibkey:
Cite (ACL):
Ji Wen, Xu Sun, Xuancheng Ren, and Qi Su. 2018. Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 365–370, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text (Wen et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-2059.pdf
Dataset:
 N18-2059.Datasets.zip