Document-Level Relation Extraction as Semantic Segmentation
Abstract
Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the problem by predicting an entity-level relation matrix to capture local and global information, parallel to the semantic segmentation task in computer vision. Herein, we propose a Document U-shaped Network for document-level relation extraction. Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples. Experimental results show that our approach can obtain state-of-the-art performance on three benchmark datasets DocRED, CDR, and GDA.
Cite
Text
Zhang et al. "Document-Level Relation Extraction as Semantic Segmentation." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/551Markdown
[Zhang et al. "Document-Level Relation Extraction as Semantic Segmentation." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/zhang2021ijcai-document/) doi:10.24963/IJCAI.2021/551BibTeX
@inproceedings{zhang2021ijcai-document,
title = {{Document-Level Relation Extraction as Semantic Segmentation}},
author = {Zhang, Ningyu and Chen, Xiang and Xie, Xin and Deng, Shumin and Tan, Chuanqi and Chen, Mosha and Huang, Fei and Si, Luo and Chen, Huajun},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {3999-4006},
doi = {10.24963/IJCAI.2021/551},
url = {https://mlanthology.org/ijcai/2021/zhang2021ijcai-document/}
}